ASSESSING SOIL MICROBIAL POPULATIONS AND ACTIVITY FOLLOWING THE USE OF MICROBIAL INOCULANTS: EFFECTS ON DISEASE SUPPRESSIVENESS AND SOIL HEALTH Except where reference is made to the work of others, the work described in this thesis is my own or was done in collaboration with my advisory committee. This thesis does not include proprietary or classified information. ___________________________ Marleny Cadena Cepeda Certificate of approval: ___________________________ ___________________________ Henry Fadamiro Joseph Kloepper, Chair Assistant Professor Professor Entomology and Plant pathology Entomology and Plant Pathology ___________________________ ___________________________ Kathy Lawrence Brett Runion Associate Professor USDA-ARS Entomology and Plant pathology National Soil Dynamics Lab ___________________________ ___________________________ Nancy Kokalis-Burelle Joe F. Pittman USDA-ARS Interim Dean U.S. Horticultural Research Lab Graduate School ASSESSING SOIL MICROBIAL POPULATIONS AND ACTIVITY FOLLOWING THE USE OF MICROBIAL INOCULANTS: EFFECTS ON DISEASE SUPPRESSIVENESS AND SOIL HEALTH Marleny Cadena Cepeda A thesis Submitted to the Graduate Faculty of Auburn University in Partial Fulfillment of the Requirements for the Degree of Master of Science Auburn, Alabama December 15, 2006 iii ASSESSING SOIL MICROBIAL POPULATIONS AND ACTIVITY FOLLOWING THE USE OF MICROBIAL INOCULANTS: EFFECTS ON DISEASE SUPPRESSIVENESS AND SOIL HEALTH Marleny Cadena Cepeda Permission is granted to Auburn University to make copies of this thesis at its discretion, upon request of individuals or institutions at their expense. The author reserves all publications rights. ___________________________ Signature of Author ___________________________ Date of Graduation iv VITA Marleny Cadena Cepeda, daughter of Cenen Cadena and Maria Elena Cepeda, was born January 22, 1972 in Santander, Colombia. Marleny has three sisters, Maribel, Jacqueline and Johanna, one brother, Alvaro, and a niece Paola. In 1988, she graduated from Externado Nacional Camilo Torres high school in Bogota, Colombia. She enrolled at Universidad Distrital at Bogota in 1989 and graduated with bachelors of physics. After working as a science teacher at the university level, she decided to study agronomy at Universidad Nacional de Colombia getting B.A. in this area in 2000. She entered graduate school at Auburn University and began her Masters of Science degree in Plant Pathology in fall 2004. She was married to Nathan Burkett, son of Brenda and Donald, on July 26, 2006 in Bogota, Colombia. v THESIS ABSTRACT ASSESSING SOIL MICROBIAL POPULATIONS AND ACTIVITY FOLLOWING THE USE OF MICROBIAL INOCULANTS: EFFECTS ON DISEASE SUPPRESSIVENESS AND SOIL HEALTH Marleny Cadena Cepeda Master of Science, December 15, 2006 (B.A., Universidad Nacional de Colombia, 2000) 123 Typed pages Directed by Joseph Kloepper In recent years, assessing use of microbial inoculants for promotion of plant growth has increased. Optimizing application frequency of PGPR is critical to achieving the maximum benefit from this technology. The study presented here, addresses the problem of developing methods for measuring soil microbial activity and population size in relation to the application of soil inoculants. Commercial formulations of PGPR, containing bacilli strains (Equity?, Soil Builder?, Ag Blend?, PGA?, Bioyield? and FZB42?) were used on tomato and strawberry in greenhouse and field experiments. Physiological activity of microbes was measured by assessing dehydrogenase activity, arylamidase activity, and fluorescein diacetate hydrolysis (FDA). Culturable microbial vi populations were determined by most probable number (MPN) and direct plate counting. In strawberry field trials, hydrolysis of FDA was significantly different among treatments at one out of four sampling times. Procedures to estimate population size (MPN) did not detect any change in microbial population; however, the use of PGPR inoculants promoted growth and increased strawberry yield. In greenhouse experiments on tomato, FDA was not always effective in measuring changes in microbial activity in the rhizosphere following of inoculants application, and arylamidase and dehydrogenase procedures were not sensitive at all. Despite detecting changes in microbial activity, no changes in microbial populations, estimated by MPN, were observed. Populations of total culturable and heat heat-tolerant bacteria were measured by plate counting. FZB42 and Bioyield treatments generally resulted in significantly greater total populations. Overall, population size measured by direct plate counts could be a useful procedure to study root colonization and persistence of introduced microorganisms in the rhizosphere. Knowing that introduced microorganisms are surviving, and their patterns of growth will help to determine when and how PGPR products should be applied. However, because of the lack of consistency the FDA procedure should not be used to decide frequency of application of PGPR products. Induction soil suppressiveness by PGPR and the relation to microbial activity and population size were also studied. The plant parasitic nematode, Meloidogyne incognita, and tomato were used as a model. Results showed significant reductions in number of nematode eggs per gram of root, number of juveniles per ml and number of galls in FZB42 and Bioyield treatments. Additionally, increases in population size were detected for those treatments by direct plate counting, although there was not a correlation between microbial activity and population size. vii ACKNOWLEDGMENTS The author would like to thank her advisor Dr. Joseph Kloepper, her committee members Dr. Kathy Lawrence, Dr. Henry Fadamiro, Dr. Brett Runion, and Dr. Nancy Kokalis-Burelle for their assistance and guidance in this research, also, John McInroy, Camilo Ramirez, and her husband Nathan Burkett-Cadena for their help in the lab and with life. viii Style manual or journal used Applied Soil Ecology Computer software used: Microsoft word? ix TABLE OF CONTENTS LIST OF TABLES????...???????????????????? x LIST OF FIGURES..???...???????????????????? xiv I. LITERATURE REVIEW???????????????????. 1 1. Soil Quality and Health?????????????????...... 1 2. Evaluating Soil Health??????????????????? 2 2.1 Microbial Biomass????????????????????.. 4 2.2 Microbial Diversity????????????????????. 5 2.3 Microbial Activity????????????????????... 6 3. Microbial Inoculants PGPR????????????????? 13 References?????????????????????????. 15 II. ASSESSING SOIL MICROBIAL ACTIVITY FOLLOWING THE USE OF MICROBIAL INOCULANTS???????????????.... 24 1. Introduction??????????????????????..... 24 2. Materials and Methods??????????????????? 27 3. Results???????????????????????......... 34 4. Discussion???????????????????????... 38 References?????????????????????????. 41 III. EFFECTS OF MICROBIAL INOCULANTS ON SOIL MICROBIAL POPULATIONS, DISEASE SUPPRESSIVENESS, AND SOIL HEALTH??????????????????????............... 68 1. Introduction??????????????????????..... 68 2. Materials and Methods??????????????????? 70 3. Results???????????????????????......... 76 4. Discussion???????????????????????... 79 References?????????????????????????. 83 IV. SUMMARY????????????????????????.. 107 x LIST OF TABLES II. ASSESSING SOIL MICROBIAL ACTIVITY FOLLOWING THE USE OF MICROBIAL INOCULANTS 2.1 Effect of commercial PGPR products on fluorescein diacetate hydrolysis (FDA) as a measure of total microbial activity in strawberry rhizosphere soil (May 2005)???????????????????????. 45 2.2 Effect of commercial PGPR products on fluorescein diacetate hydrolysis (FDA) as a measure of total microbial activity in strawberry rhizosphere soil (June 2005)???................................................................................. 46 3.1 Effect of Commercial PGPR products on strawberry plant growth (May 2005)????????????????????????.... 47 3.2 Effect of Commercial PGPR products on strawberry root architecture (May 2005)????????????????????????? 48 3.3 Effect of Commercial PGPR products on strawberry root architecture (June 2005)????????????????????????? 49 3.4 Effect of Commercial PGPR products on strawberry yield...?????... 50 4.1 Effects of commercial (PGPR) products on fluorescein diacetate hydrolysis as a measure of total microbial activity in tomato rhizosphere soil???????..?????????????........... 51 4.2 Effect of commercial PGPR products on total microbial activity measured by dehydrogenase activity in tomato rhizosphere soil????????.. 52 4.3 Effect of commercial PGPR products on total microbial activity measured by dehydrogenase activity in tomato rhizosphere soil????????.. 53 4.4 Effect of commercial PGPR products on microbial activity measured by Arylamidase activity in tomato rhizosphere soil???????..?......... 54 5.1 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements one day after inoculation??? 55 xi 5.2 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements three days after inoculation?????????????????????????. 56 5.3 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements five days after inoculation?????????????????????????. 57 5.4 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements seven days after inoculation?????????????????????????. 58 5.5 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements ten days after inoculation?????????????????????????. 59 5.6 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements one day after re-inoculation??...?????????????????????. 60 5.7 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements three days after re-inoculation???????????????????????? 61 5.8 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements five days after re-inoculation???????????????????????? 62 5.9 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements seven days after re-inoculation???????????????????????? 63 5.10 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements ten days after re-inoculation???????????????????????? 64 5.11 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements fifteen days after re-inoculation???????????????????????? 65 xii III. EFFECTS OF MICROBIAL INOCULANTS ON SOIL MICROBIAL POPULATIONS, DISEASE SUPPRESSIVENESS, AND SOIL HEALTH 2.1 Effect of commercial PGPR products on fluorescein diacetate hydrolysis (FDA) using procedure suggested by Schnurer and Rosswall as a measure of total microbial activity in tomato rhizosphere soil????????... 88 2.2 Effect of microbial inoculants PGPR on total bacteria, total heat-tolerant bacteria, and total fluorescent pseudomonads???????????.. 89 2.3 Effect of FZB42 on fluorescein diacetate hydrolysis (FDA) using procedure suggested by Green et al. (2006) as a measure of total microbial activity in tomato rhizosphere soil??????????.?.. 90 2.4 Effect of Bioyield on fluorescein diacetate hydrolysis (FDA) using procedure suggested by Green et al. (2006) as a measure of total microbial activity in tomato rhizosphere soil???????????? 91 2.5 Effect of FZB42 on total bacteria, total heat-tolerant bacteria, and fluorescent pseudomonads??????????????????? 92 2.6 Effect of Bioyield on total bacteria, total heat-tolerant bacteria, and fluorescent pseudomonads??????????????????? 93 3.1 Effect of microbial inoculants (PGPR) on root architecture related variables one day after inoculation???????????????? 94 3.2 Effect of microbial inoculants (PGPR) on plant growth measurements one day after inoculation??.??????????????????? 95 3.3 Effect of microbial inoculants (PGPR) on root architecture related variables five day after inoculation?.??????????????.. 96 3.4 Effect of microbial inoculants (PGPR) on plant growth measurements five day after inoculation?????????????????????.. 97 3.5 Effect of microbial inoculants (PGPR) on root architecture related variables ten day after inoculation???????????????? 98 3.6 Effect of microbial inoculants (PGPR) on plant growth measurements ten day after inoculation?????????????????????. 99 3.7 Effect of microbial inoculants (PGPR) on root architecture related variables fifteen day after inoculation??????????????... 100 xiii 3.8 Effect of microbial inoculants (PGPR) on plant growth measurements fifteen day after inoculation???????.............................................. 101 4.1 Effect of three PGPR microbial inoculants on nematodes and plant growth??.................................................................................................... 102 4.2 Effect of three PGPR microbial inoculants on nematodes and plant growth??????????????????????????? 103 4.3 Effect of three PGPR microbial inoculants on nematodes and plant growth??????????????????????????? 104 5.1 Effect of three PGPR microbial inoculants on total bacteria, total heat- tolerant bacteria, and FDA hydrolysis in tomato rhizosphere soil???? 105 5.2 Effect of three PGPR microbial inoculants on total bacteria and total heat- tolerant bacteria in tomato rhizosphere soil?...??????????... 106 xiv LIST OF FIGURES 1 Effect of commercial PGPR products on total microbial activity measured by FDA hydrolysis in the tomato rhizosphere soil?????????? 66 2 Effect of commercial PGPR products on culturable bacterial populations in the tomato rhizosphere???????????????????.. 67 1 LITERATURE REVIEW 1. Soil Quality and Health The terms soil quality and soil health have both been used to describe agriculturally productive soils. However various authors use the terms differently. Clarifying the distinction between soil quality and soil health is essential for developing a clear understanding of soil and rhizosphere systems. Soil health refers to the soil as an ecosystem, involving biodiversity and sustainable production; therefore, it may be defined as the capacity of a soil ecosystem to function as a living and dynamic system to uphold biological productivity over time (Harris et al., 1996). Implied in this definition of soil health are the key concepts of diversity, structure, function and performance of soil. Accordingly, soil health focuses primarily on the soil?s continued capacity to sustain plant growth as indicated by various essential physical, chemical and biological processes in the soil ecosystem. Because soil health depends upon these processes, specific indicators may be used to quantify or assess soil health (Doran, 2002). In contrast to soil health, soil quality is a term used to indicate a specific purpose of the soil. Hence, soil quality can vary depending upon land use, such as forest plantation, or production of agricultural or horticultural crops. Also, it focuses on the capacity to meet defined needs such as the growth of a particular crop (Doran, 1994a; Doran, 1994b). 2 Thus, it is not possible to generalize by saying that good soil health always results in high quality or vice versa. For example, a rain-forest soil is healthy based upon indicators of biological diversity but it has low quality for growing crops. However, soil health typically considers soil quality because soil health includes the long-term sustainability of soil for crop production. In summary, soil health involves the care of plants and animals, water and air quality. Soil viewed as an ecosystem will be used under sustainable conditions of plant productivity, while soil quality considers optimizing soil conditions for one particular desired purpose or use. Although the two terms are conceptually different yet related, I will use soil health instead of soil quality, as a descriptive term for soils since maintaining and enhancing soil health is imperative to sustaining agricultural productivity. 2. Evaluating Soil Health A framework is needed for evaluating soil health. Such a framework would help to identify problems in production areas and to monitor changes in the environment related to agricultural management. Larson and Pierce (1994) proposed that a minimum data set of soil parameters should be adopted for assessing soil health and that standardized methods and procedures should be established to assess changes in those factors. A set of basic indicators has not previously been defined, mainly because researchers have not reached agreement on which indicators should be measured to fully assess soil health. It is generally accepted that such indicators should be easy to calculate and responsive to variations in management (Visser and Parkinson, 1992). 3 Establishing standardized indicators is problematic because they are typically related to a specific management system and do not generally function or exist in all soils. The intricate link among biological, physical and chemical properties of soil requires more than a single factor to assess soil health. For example, it is not sufficient to use only organic matter content, which is generally related to soil quality, for specifying the overall health of a soil (Bending et al, 2003). Thus, parameters independent from soil type and environment are needed. In the past, several methods have been employed to assess the complexity of the relationships among the physical, chemical and biological properties of soil. Karlen and Stott (1994) used a systematic method to associate soil function with soil quality. They defined a soil of high quality in terms of water entrance, transfer and absorption, resistance to surface degradation and support of plant growth. Glover et al, (2000) applied this method to evaluate the effect of conventional, organic, and integrated apple production systems on the soil?s physical, chemical, and biological properties by using a soil quality index and found it effective for the purpose of that study. As previously stated, soil health considers the chemical, physical and biological processes of the living soil ecosystem. In searching for indicators of soil health, while chemical and physical properties make an important contribution and may determine the quality of a specific soil, one could consider that biological indicators would also be key. Biological indicators monitor the living portion of the ecosystem and are most susceptible to change and therefore to degradation by agricultural practices. Although, microorganisms occupy only 5 % of the soil, they constitute a large microbial biomass and diversity, and together with plants, make up the major biological portion of soils and 4 rhizospheres (Brady and Weil, 2002). The rhizosphere is of special interest since the soil-root interface also influences ecological conditions, and the metabolic activity in the rhizosphere is higher than in the surrounding soil (Schinner, et al., 1996). It has been suggested consequently, that measurements of soil and rizhosphere microorganisms could be useful indicators of soil health. 2.1 Microbial Biomass Microbial indicators that have been suggested to monitor soil health are microbial biomass, microbial activity and microbial diversity. Soil microbial biomass is the total microbiological component of soil and it is generally expressed in milligrams of carbon per gram dry weight of soil. Methods to estimate soil microbial biomass have been studied and well developed over the last two decades due to of the increasing demand for precise quantitative measurements of soil microbial processes. Microbial biomass can be a sensitive indicator of changes in soil processes because it has a much faster rate of turnover than total organic matter (Jenkinson et al., 2004). Microbial biomass and activity measures have been used to monitor soil recovery and to show effects of toxic residues on soil. They have also given information about the effects of cropping, rotation and cultivation practices on soil health (Limon-Ortega et al., 2006). Some authors have suggested a strong link between soil microbial biomass, fertility and health (Vanlauwe et al., 1999; Ladd, et al., 2004; Villar et al., 2004). Biochemical methods for biomass measurements are based on microbial membrane components (phospholipids, lipopolysaccharides, and ergosterol) and constituents of microbial cell wall (peptidoglican and chitin). Such methods have been 5 used to estimate microbial biomass in soils. (Zelles et al. 1992; Joergensen, 1996; Vanlauwe et al. 1999; Jenkinson et al., 2004; Villar, et al, 2004). 2.2 Microbial Diversity In addition to studying microbial biomass, one can examine soil microbial diversity and community structure in relation to soil health. Soil microbial diversity and community structure are the different fungal and bacterial species and their relative abundance in the soil community (Schinner et al, 1996). Microbial diversity is measured by various techniques such as traditional plate counting and direct counts, fatty acid analysis, and the newer molecular-based procedures. Direct counting is a method used to quantify bacteria. The plate count technique, fluorescence microscopy, and most probable number method are among the most utilized. The disadvantage of direct counting is that there is no discrimination between living and dead microbial cells and it does not enable the count of specific microbial species (Nannipieri et al, 2003). Molecular techniques generally involve extraction of nucleic acid, directly or indirectly, from soil. They are independent of culture and, according to their sensitivity, can detect species, genera, families or even higher taxonomic groups (Nannipieri et al, 2003). The denaturizing gradient gel electrophoresis (DGGE) method has been utilized to investigate distribution, diversity, and changes of microbial communities in forest and mineral soils (Agnelli et al. 2004; Villar et al, 2004). The community structure in soil can also be assessed by measuring lipids and phospholipids (Zelles et al. 1992). These molecular techniques give an overall indication of microbial diversity and can be used to 6 assess soil health and to monitor changes in the composition of a microbial community after stress or changes in management. Some research studies have found correlations among microbial biomass, activity and, diversity. Leckie, et al. (2004) evaluated the relationships between CFE (chloroform fumigation-extraction), PLFA (phospholipid fatty acids), and DNA methods in forest humus, and found a good relationship between PLFA and CFE. Agneli et al, (2004) studied the composition of fungal and bacterial communities in a forest soil by soil microbial biomass (SMB), DGGE, SIR (substrate induced respiration), and DNA. They found high bacterial diversity in the upper layer of the soil, decreasing with depth, but a low fungal diversity. Bailey et al (2002) studied the relationship between CFE, SIR, and PLFA to assess SMB and they found PLFA was best predicted by CFE, and they developed a conversion factor. Marstorp and Witter (1999) measured microbial growth in soil after the addition of glucose. Their results suggested that increases in respiration accompanied increases in the amount of dsDNA from soil, and therefore, the technique could be an alternative to measure CFE. Vanlauwe et al (1999) measured SMB as total LP (lipopolysaccharides) in soils with different organic contents. 2.3 Microbial Activity Soil microbial activity is different from soil microbial biomass. Soil microbial biomass is used to quantify populations and also can be used to assess the dynamics of nutrients in soil. For example, the determination of microbial C, N, P and S contents by fumigation techniques have allowed a better quantification of nutrient dynamics in soil. While soil microbial biomass describes the total microbial population size, soil microbial activity indicates the vast range of physiological activities carried out by soil 7 microorganisms. The term soil enzyme activity is related to microbial activity and reflects the physiological work of all living organisms in the soil, including plant roots (Ladd, 1978). In this review, a distinction will be made between enzymatic activity and microbial activity. Soil enzymes are not only related to physiological activity of microorganisms but also to such activity of animals and plants. Further, enzymes in soil are not always bound to active cells, as they can be adsorbed to clays or humic colloids (Skujins, 1978). Nevertheless, soil enzymes are relevant for assessing soil health because they are essential for organic matter turnover and the metabolic activity of soil microorganisms (Nannipieri et al., 2002). Several methods have been used to determine soil microbial activity. Some methods measure the rate of entire metabolic processes such as evolution of CO 2 (respiration), nitrification activity, DNA synthesis in bacteria, fluorescein diacetate (FDA), and activity of dehydrogenase (Nannipieri et al., 1990). Other methods measure specific activities of either a particular enzyme or a set of enzymes that are involved in a particular metabolic pathway of interest, such as chitinases, cellulases, and trehalases (Tabatabai and Deng, 1994; Anderson et al., 2004; Pavel, et al., 2004). a. CO 2 Evolution Soil respiration is defined as oxygen uptake or carbon dioxide evolution by bacteria, fungi, algae, nematodes and protozoan, and includes the gas exchange of aerobic and anaerobic metabolism (Anderson, 1982). Soil respiration results from the degradation of organic matter, with the formation of CO 2 occurring in the last step of carbon mineralization. When soil is disturbed, a change in soil respiration can be observed due to more rapid growth and higher mineralization of the microorganisms 8 (Singh and Gupta, 1977). This respiration is characterized by several phases including an increase, exponential acceleration, delay, stationary and a decline phase. Respiration is called basal respiration when there is a balance between microorganism and their activities in undisturbed soils. CO 2 evolution from a soil is thus a measure of the total soil biological activity, including microbial activity (Alef and Nannipieri, 1995). Substrate induced respiration (SIR) is a method used to measure respiration, after the addition of glucose as a substrate to the soil sample, with the change in respiration being measured after 8 h. SIR is a measure of soil microbial activity, but it can also be used to estimate soil microbial biomass. The maximum initial respiratory response is proportional to the amount of microbial carbon present in the soil sample. Therefore, using a conversion factor, the respiration value can be converted to mg of soil biomass carbon; however, SIR is most accepted as a measure of microbial activity (Anderson, 1973). b. Soil Enzyme Procedures Soil enzyme activity may serve as an indicator of soil health. As showed by Dick (1997), soil enzymes are mediators of innumerable processes in cells and catalysts of functions that include transformation of organic matter, release of organic nutrients for plant growth, nitrogen fixation, detoxification, nitrification, and de-nitrification. Assessing soil enzymes is a potential indicator of the biological status or the capacity of soil to carry out the enzyme-catalyst processes. Soil enzymes are present inside microbial cells (intracellular) and outside of the cell (abiontic). One difficulty in studying enzymes is that only a small proportion of the total enzyme pool can be extracted from soils. Also, substrates must be added to quantify 9 enzymes in various assays. These assays can underestimate concentration because they are done in optimum media, and under conditions of controlled pH, temperature, and moisture. Another difficulty is that separating living cells from abiontic enzymes is not always possible. Despite the difficulties listed above, soil enzymes have been effectively used to monitor soil quality, investigate soil microbial activity (Anderson et al, 2004), estimate soil resilience to wastes (Benitez et al., 2004), and evaluate soil after fumigation with methyl bromide (Klose and Ajwa, 2004). Soil enzymes include oxidoreductases, transferases, hydrolases and lyases. The enzymes most often reported to be active in soil include protease, urease, phosphatase, cellulase, b-glucosidase, saccharase, xylanase, catalase, dehydrogenase, amylase, and pectinase (Alef and Nannipieri., 1995). Proteases catalyze the hydrolysis of proteins to polypeptides and oligopeptides to amino acids; they are found in living cells, dead cells, as free enzymes, and adsorbed to organic and inorganic particles. Protease activity is significantly correlated with SIR, dehydrogenase, and aggregate stability (Kandeler and Murer, 1993). Ureases catalyze the hydrolysis of urea to CO 2 and NH 3 with a reaction mechanism based on the formation of carbamate as intermediator. In soil, ureases are tightly bound to soil organic matter and minerals. Urease activity is not correlated with microbial biomass but is affected by heavy metals, oxygen concentration, and nitrogen availability in different types of soils (Tabatabai and Bremner, 1972; McCarty and Bremner, 1991; McCarty et al, 1992). Phosphatases catalyze the hydrolysis of a variety of organic phosphomonoesters and are important in mineralization of soil P. Benitez, et al. (2004), measured enzymatic activity (Hydrolytic- phosphatase, b-glucosidase, oxidoreductase-dehydrogenase, 10 diphenol oxidase-IAA production) to estimate soil resilience to a toxic organic wastes. However, the results of this research indicate that only dehydrogenase, diphenol and ?- glucosidase can be used to monitor changes. Cellulases are a group of enzymes that catalyze the hydrolysis of cellulose to glucose, cellobiose, and higher oligosaccarides; fungi mainly produce this enzyme. Anderson et al, (2004) studied microbial enzyme activity in leaf litter, humus and mineral soil layers. They have reported no correlation among cellulase activity, basal respiration, and microbial biomass carbon, while there was a positive correlation with chitinase activity. Soil management related to the activity of eleven soil enzymes including cellulase was investigated. Results showed a significant positive impact by the cultivation system on each enzyme tested, especially where cover crops or organic residues were added (Bandick and Dick, 1999). Fluorescein Diacetate Hydrolysis Fluorescein diacetate is a colorless compound hydrolyzed by both free and membrane-bound enzymes, resulting in the release of fluorescein. This end product absorbs strongly in the visible wavelength and can be measured by spectrophotometry. Several enzymes, such as non-specific esterases, proteases, and lipases, are responsible for FDA hydrolysis and are plentiful in the soil environment (Schnurer and Rosswall, 1982). Adam and Duncan (2000) adapted the method from the original for the measurement of the total microbial activity in soils. A chloroform/methanol (2:1 v/v) solution is added immediately to terminate the reaction, instead of acetone. However, Green et al. (2006) have recently optimized the FDA method with the advantage of using 11 static incubation and less solvent to terminate the hydrolysis, and also covering a large range of activity. The FDA method is recommended for its sensitivity, simplicity and precision to be used for studies of soil microbial activity. FDA hydrolysis has been correlated with some of the most accurate measures of microbial biomass, ATP content and density studies (Federle and Ventullo, 1990). In addition FDA hydrolysis was found to be effective in determining how alternatives to methyl bromide affect microbial activity (Fernandez, et al. 2001). Dehydrogenase Activity Dehydrogenase reflects the total oxidative activities of soil microorganisms (Alef and Nannipieri, 1995). The biochemical properties of dehydrogenases are such that free, abiontic quantities are not expected to be present in soil. Dehydrogenase activity is considered to be an indicator of biological oxidation/ reduction reactions and therefore can be used as a measure of the intensity of microbial metabolism in soil (Skujins, 1978). Dehydrogenase activity is assayed on 1 g oven-dry equivalents of buffered soil solution incubated for 48 h at 27?C after addition of a specific substrate 2, 3, 5- triphenyltetrazolium chloride (TTC) (Lenhard, 1956). The product of the reaction, triphenyl formazan (TPF), is measured colorimetrically. Benitez, et al, (2000) studied enzyme activities in the rhizosphere of pepper. Dehydrogenase, as well as urease and phosphatase, activity were evaluated. Their results showed an increase of rhizosphere dehydrogenase activity after the addition of mulches and important effects of the incorporation of organic materials on urease and phosphatase activity. Klose and Ajwa, (2004) studied enzyme activities in agricultural soils after fumigation with methyl bromide alternatives. Their results showed a decrease of the activities of ?-glucosidase, 12 dehydrogenase, and acid phosphatase; however, alternative fumigants had no significant effect on enzyme activity. Arylamidase Activity The enzyme arylamidase catalyzes the release of an N-terminal nitrogen amino acid from peptides, amides or arylamides. This enzyme is also probably involved in N mineralization. The method to evaluate arylamidase activity is based on the colorimetric determination of the B-naphthylamine produced when soil samples are incubated with L- leucine B-naphthylamide in 0.1 M THAM buffer solution at 37? C for 1 h. The B- naphthylamine produced is extracted with ethanol and converted into an azo compound, and the absorbance of the color is measured at 540 nm (Acosta-Martinez and Tabatabai ., 2000a). The importance of this enzyme in soil is related to its role in the release of amino acids from soil organic matter. The amino acids released by this enzyme are substrates for amidohydrolases. Therefore, arylamidase enzyme activity has been significantly correlated with soil organic C content and with the activity of L-asparaginase, L- aspartase, urease and amidase (Acosta-Martinez and Tabatabai, 2000b). In general, FDA hydrolysis, dehydrogenase and arylamidase enzymatic procedures may have potential to be used as indicators of microbial activity since they reflect a wide range of physiological activities carried out by soil microorganisms. It is important to highlight the relation between enzymatic activity and soil health. In this respect, soil enzymes have a unique and appropriate role in assessing soil health because soil can be thought of as a living biological entity where several biochemical reactions, mediated by enzymes, are taking place. 13 3. Microbial Inoculants (PGPR) The rhizosphere, the zone of soil under the influence of the root (Uren, 2000), is characterized by high microbial diversity, activity, number of organisms, and complex interactions between microorganisms and the roots (Cheng et al, 1996; Oger et al, 2004). To measure the effect of the rhizosphere on a particular population, the number of microorganisms in the rhizosphere (R) and the number of microorganism in the bulk soil (S) are compared. This R/S ratio provides an estimate of how strongly the rhizosphere affects a particular organism. This ratio also determines the rhizosphere competence and if an organism has a good rhizosphere competence, it could be used as a microbial inoculant (Pinton et al, 2000). Bacteria are the most numerous inhabitants in the rhizosphere, typically numbering 10 6 to 10 8 organism g -1 of rhizosphere soil, although they account for only a small portion of the total biomass. The concentration of bacteria in the rhizosphere is higher than in bulk soil due to the production of root exudates that can support bacterial growth and metabolism (Bais et al, 2006). The interaction between bacteria and plant roots may be beneficial, harmful, or neutral for the plant. The bacteria that provide benefits to the plant are of two general types, those that form a symbiotic relationship, which involves the formation of specialized structures as in the genus Rhizobia, and those that are free-living in the soil. Beneficial free-living bacteria, referred to as plant growth-promoting rizhobacteria (PGPR) have been found in association of many different plants. PGPR can impact plant growth directly and indirectly (Whipps, 2001). The direct promotion of 14 plant growth by PGPR occurs when bacteria provide the plant with a compound that is synthesized by the bacterium or facilitating the uptake of nutrients from the environment. On the other hand, the indirect growth promotion is related with antagonism and induced systemic resistance (Glick, 1995). Antagonism, one of the major groups of mechanisms of biocontrol, is defined by Cook and Baker (1983) as the actively expressed opposition, which includes antibiosis, competition and parasitism. While induced resistance refers to the activation of the host plant?s chemical or physical defenses by an inducing agent and may be systemic or localized within the plant. Thus, induced systemic resistance is used to describe the process whereby treatment of plants with PGPR elicits a host defense as indicated by reduction in the severity or incidence of diseases caused by pathogens that are spatially separated from the inducing agent (Kloepper et al, 2004). In some reports induced systemic resistance has been associated with enhancement of lignification and increases in peroxidase and superoxide dismutase activity (koike et al, 1997). PGPR have been widely used in agriculture. The results of numerous studies on various crops conducted over the past two decades generally showed beneficial effects for increasing yield, germination rate, tolerance to drought, and shoot and root weights (Lucy et al, 2003). Another major benefit is their use as biological control agents of plant disease organisms (Zehnder et al, 2001). Several PGPR inoculants are currently commercialized to promote growth using one of several mechanisms; suppression of plant disease, improvement of nutrient acquisition, or phyto-hormone production. Inoculants suppress plant diseases through the induction of systemic resistance, and production of siderophores or 15 antibiotics (Zahir et al., 1998). Examples of microbial inoculants are Bioyield?, which contains a mixture of two strains (Bacillus subtilis and B. amyloliquefaciens) and Soil Builder?, Ag Blend?, and Equity?, which contain complex mixtures of over 10 strain of Bacillus. Since the rhizosphere is considered the most intense ecological habitat in soil, it is of interest to study the effects that PGPR may have on the total microbial activity and bacterial population in the zone where rhizobacteria exerted a direct influence on plants. The objectives of this study were to develop a set of sensitive methods to detect increases in microbial activity following additions of microbial inoculants and to determine the relationship among soil microbial activity, microbial population and disease suppressiveness after the addition of soil inoculants PGPR. References 1. Adam, G., Duncan, H., 2000. 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Kloepper, J.W., Ryu, C.-M., Zhang, S., 2004. Induced systemic resistance and promotion of plant growth by Bacillus spp. Phytopathology 94: 1259-1266. 33. Klose, S., Ajwa., H. A., 2004. Enzyme activities in agricultural soils fumigated with methyl bromide alternatives. Soil Biology and Biochemistry 36(10), 1625- 1635. 34. Koike, N., Kageyama, K., Hyakumachi, M., Tsuyumu, S., Park, H., Doke, N., 1997. Lignification and superoxide generation elicited in plant tissues by culture filtrates of plant growth-promoting fungi (PGPF) associated with induced systemic resistance. In: Plant Growth-promoting rhizobacteria: Present status and future prospects, (Eds.) A, Ogoshi, K. Kobayashi, Y. Homma, F. Kodama, N. Akino, OECD, Paris, pp 269-272. 35. Ladd, J.N., 1978. Origin and range of enzymes in soil. In: R.G. Burns (Eds.) Soil enzymes. Academic press, New York, pp 51-80. 20 36. Ladd, J.N., Amato, M., Veen, H.A., 2004. Soil microbial biomass: its assay and role in turnover of organic matter C and N. Soil Biology and Biochemistry 36, 1369-1372. 37. Larson, W. E., Pierce F. J.,1994. The dynamics of soil quality as a measure of sustainable management. In: Doran, J.W. Jones, A.J.(Eds.), Defining soil quality for a sustainable environment. Soil Science Society of America Special publication # 35. SSSA, Madison, WI, USA, pp. 37-52. 38. Leckie, S.E., Prescott, C.E., Grayston, S.J., Neufeld, J.D., Mohn W.W., 2004. Comparison of chloroform fumigation-extraction, phospholipid fatty acid, and DNA methods to determine microbial biomass in forest humus. Soil Biology and Biochemistry 36(3), 529-532. 39. Lenhard, G., 1956. The dehydrogenase activity in soils as a measure of the activity of soil microorganisms. Z pflanzenernah Dung Bodenkd 73. 1-11. 40. Limon-Ortega, A., Govaerts, B., Deckers, J., SAyre, K.D., 2006. Soil aggregate and microbial biomass in a permanent bed wheat-maize planting system after 12 years. Field Crop Research 97, 302-309. 41. Lucy, M., Reed, E., Glick, B.R., 2003. Applications of free living plant growth- promoting rhizobacteria. Antone van Leeuwenhoek 86, 1-25. 42. Marstorp, H., Witter, E., 1999. Extractable dsDNA and product formation as measures of microbial growth in soil upon substrate addition. Soil Biology and Biochemistry 31(10), 1443-1453. 21 43. McCarty, G.W., Bremner, J.M., 1991. Production of urease by microbial activity in soils under anaerobic and aerobic conditions. Biology, Fertility and Soils 11, 228-230. 44. McCarty, G.W., Shogern, D.R., Bremner, J.M., 1992. Regulation of urease production in soil by microbial assimilation of nitrogen. Biology, Fertility and Soils 12, 261-264. 45. Nannipieri, P., Ceccanti, B., Grego, S., 1990. Ecological significance of the biological activity in soil. In: Stotzky, G. and Bollag, J.M., Editors, Soil Biochemistry vol. 6, Marcel Dekker, New York, pp. 293?355. 46. Nannipieri, P., Kandeler, E., Ruggiero, P., 2002. Enzyme activity and microbiological and biochemical processes in soil. In: Enzymes in the environment. Activity, Ecology and Applications. Burns, R.G., Dick, R.P. Marcel Dekker, Inc. New York. pp 1-34. 47. Nannipieri, P., Ascher, J., Ceccherini, M.T., Landi, L., Pietramellara, G., Renella, G., 2003. Microbial diversity and soil functions. European Journal of Soil Science 54, 655?670. 48. Oger, P. M., Mansouri, H., Nesme, X., Dessaux, Y., 2004. Engineering root exudation of Lotus toward the production of two novel carbon compounds leads to the selection of distinct microbial population in the rhizosphere. Microbial Ecology 47, 96-103. 49. Pavel, R., Doyle, J., Steinberger, Y., 2004. Seasonal patterns of cellulase concentration in desert soil. Soil Biology and Biochemistry 36, 549-554. 22 50. Pinton, R., Varanini, Z., Nannipieri, P., 2000. The rhizosphere: Biochemistry and organic substances at soil-plant interface. Master Dekker (Eds.). New York. pp.1- 19. 51. Singh, J.S., Gupta, S.R., 1977. Plant decomposition and soil respiration in terrestrial ecosystems. Botanical Rev. 43, 449-528. 52. Schinner, R., Ohlinger, A., Kandeler, E., Margesin, R., 1996. Methods in soil biology. Springer et al (Eds.), 426 pp. 53. Schnurer, J., Rosswall, T., 1982. Fluorescein diacetate hydrolysis as a measure of total microbial activity in soil liter. Applied Environmental Microbiology 43, 1256-1261. 54. Skujins, J., 1978. History of abiontic soil enzyme research. In R.G. Burns (Eds.) Soil Enzymes. Academic Press, New York, pp. 1-49. 55. Tabatabai, M.A., Bremner, J.M., 1972. Assay of urease activity in soil. Soil Biology and Biochemistry 4, 479-487. 56. Tabatabai, M.A., Deng, S.P., 1994. Cellulase activity of soils. Soil Biology and Biochemistry 26 (10), 1347-1354. 57. Uren, N., 2000. Types, amounts and possible functions of compounds released into the rhizosphere by soil-grown plants. In: Pinton, R., Varanini, Z., Nannipieri, P., The rhizosphere: Biochemistry and organic substances at soil-plant interface. Master Dekker (Eds.). New York. pp. 19-37. 23 58. Vanlauwe, B., Nwoke, O.C., Sanginga. N., Merckx, N., 1999. Evaluation of methods for measuring microbial biomass C and N and relationships between microbial biomass and soil organic matter particle size classes in West-African soils. Soil Biology and Biochemistry 31 (8), 1071-1082. 59. Villar, M.C., Petrikova, V., Diaz-Ravi?a., Carballas, T., 2004. Changes in soil microbial biomass and aggregate stability following burning and soil rehabilitation. Geoderma 122 (1), 73-78. 60. Visser, S., Parkinson, D., 1992. Soil biological criteria as indicator of soil quality: soil microorganism. American Journal of Alternative Agriculture 7, 33-37. 61. Whipps, J.M., 2001. Microbial interaction and biocontrol in the rhizosphere. Journal of Experimental Botany 52, 487-511. 62. Zahir, A., Arshad, A., Frankenberger, T., 1998. Plant growth promoting rhizobacteria: Applications and perspectives in agriculture. Advances in Agronomy 5, 40-43. 63. Zelles, L., Bai, Q. Y., Beck, T., Beese, F., 1992. Signature fatty acids in phospholipids and lipopolysaccharides as indicators of microbial biomass and community structure in agricultural soils. Soil Biology and Biochemistry 24 (4), 317-323. 64. Zenhder, G.W., Murphy, J.F., Sikora, E.J., Kloepper, J.W., 2001. Application of rhizobacteria for induced resistance. European Journal of Plant Pathology 107, 39-50. 24 ASSESSING SOIL MICROBIAL ACTIVITY FOLLOWING THE USE OF MICROBIAL INOCULANTS 1. Introduction Soil is a highly complex system characterized by a variety of biological, chemical and physical processes, which are markedly influenced by environmental factors (Alef et al., 1995). Microorganisms influence soil productivity by recycling carbon, nitrogen and other minerals. Therefore, numerical abundance, fast reproductive rates, diversity of type and metabolic activity, and tolerance to a wide range of environmental conditions are key characteristics of soil microbial populations (Doran, 2002). In order to understand heterogeneity of soil, there is a need for suitable methods for studying interactions between environmental factors and microbial populations and activity in soil. Plant growth promoting rhizobacteria (PGPR) are beneficial bacteria that colonize the rhizosphere, the surface of the root or even superficial intercellular spaces (McCulley, 2001). Applications of PGPR are usually accompanied by enhancement of plant growth or protection against certain pathogens. Several products containing PGPR strains are commercially available. However, very little is known about the influence of these inoculants on the soil ecosystem. The term microbial activity comprises all biochemical reactions catalyzed by microorganisms in soil. Some reactions, such as respiration and heat output, can be 25 conducted by most soil microorganisms while others, such as nitrification and nitrogen fixation, can only be conducted by a restricted number of microbial species (Alef et al., 1995). In addition, differences in rhizo-deposition, plant species, age and stage of development may have directly influence microbial activity and population. Consequently, the approach of measuring overall microbial activity after application of PGPR may distinguish between treated and untreated rhizosphere soil. Total microbial activity provides a general measure of organic matter turnover in natural habitats as about 90% of the energy in the soil environment flows through microbial decomposers (Heal and MaClean, 1975). There are several enzymatic methods for measuring total microbial activity. One method is fluorescein diacetate (3 ? 6?- diacetiylfluorscein (FDA) hydrolysis (Schnurer and Rosswall, 1982). Green et al. (2006) have optimized the method to assay FDA in soils by using a static incubation, reducing solvent to terminate the hydrolysis, and covering a large range of activity. As a result, the optimized method for FDA in soil samples can be used as a biochemical and biological indicator of soil quality. FDA is hydrolyzed by a number of different enzymes such as proteases, lipases and esterases. The product of this enzyme conversion is fluorescein, which can be seen within the cells by fluorescence microscopy or quantified by spectrophotometry (Schnurer and Rosswall, 1982). The equation of the reaction follows. 26 Another enzymatic assay to measure total microbial activity is through dehydrogenase activity. Dehydrogenase activity reflects the total oxidative activities of soil microorganisms, which is important in oxidizing soil organic matter. Dehydrogenase activity has been used as an indicator of microbial activity in response to successive addition of toxic organic wastes (Benitez et al, 2004). One of the most frequently used methods to estimate dehydrogenase activity in soil is based on the use of thiphenyltetrazolium chloride (TTC) as an artificial electron acceptor (Lenhard, 1956). The TTC is reduced to triphenil formazan (TPF) (Smith and Pugh, 1979). Nearly all microorganisms reduce TTC to TPF, which can be spectrophotometrically measured. The equation for the reaction follows (Alef and Nanniperi, 1995). Another enzyme with potential to measure total microbial activity in soil is arylamidase {?-aminoacyl-peptide hydrolase}, which is normally found in animal tissues and fluids (Hiwada et al, 1980) and has been recently detected in soil. An accurate method has also been developed for its assay (Acosta-Martinez and Tabatabai, 2000). 27 Arylamidase is capable of hydrolyzing the neutral amino acid ?-naphthylamides or p- nitroanilides. The equation for the reaction using the amino acid L-leucine, as an example, is shown bellow. The importance of studying the arylamidase activity in soil is related to its role in the N- cycling process (Stevenson, 1994). In this study, FDA hydrolysis, dehydrogenase, and arylamidase activity were used as indicators/detectors of increases in microbial activity following the addition of PGPR microbial inoculants. Criteria for choosing enzyme assays were based on previous experiments with their sensitivity to detect soil microbial activity, importance in nutrient cycling and organic matter decomposition, and simplicity of the assay. Fluorescein diacetate hydrolysis (FDA) can be hydrolyzed by many enzymes (lipases, esterases and proteases) and organisms. Thus this assay provides a broad ?spectrum indicator of soil biological activity. Dehydrogenases were chosen for their critical role in oxidative activities of soil microorganisms. Arylamidases were included because of their role in releasing inorganic N in the N cycle. 2. Materials and Methods A. Field Studies Field experiments were conducted at the Sand Mountain Research and Extension Center in spring 2005 to evaluate the effect of the application of microbial inoculants on 28 soil microbial activity and strawberry root architecture. Strawberry transplants were produced by Lewis Nursery (North Carolina); transplants were planted in the field in October 2004 and harvested in spring 2005. Plants were fertilized monthly with a solution of 20-20-20 fertilizer. The treatments consisted of three different inoculants and a water control. All of the microbial treatments were applied at the time of transplanting by using 34 g per liter at 1% aqueous suspension of the product and all subsequent times at 0.74 liters per hectare through the drip irrigation system. Experimental Design: The experimental design was a randomized complete block (RCB) with four treatments and six replications. Each plot contained 40 transplants in double rows. Treatments were (1) Ag Blend?, (2) Soil Builder?, (3) Equity? and (4) nontreated control (Table 1). Soil samples were taken once a month 10 days after the monthly microbial inoculation. Soil Sampling Soil was collected from the experimental plots 10 days after microbial inoculation. For each replication, five sub-samples were taken with a soil core borer to a depth of 20 cm in the strawberry root zone. Soil samples were then thoroughly mixed, placed in plastic bags, sealed, and deposited in a container with ice and brought to the laboratory. Soil samples were stored at 4?C for four days and then sieved before measuring microbial activity. 29 Table 1. Commercial PGPR Products Microbial Inoculants (PGPR) Description Dose Naturize Equity? Contains 47 strains of bacilli in a liquid formulation. 0.48g/L water Organica Plant Growth Activator (PGA) ? Contains 54 strains of bacilli, Pseudomonads, Actinomycetes, and Trichoderma in a powder carrier. 1 tablespoon/Gal water Super bio Soil Builder? Contains bacteria (Bacillus spp., actinomycetes, cyanobacteria, and others), algae, and protozoa in a liquid carrier. 118.4ml/Gal water Super bio Ag Blend? Contains multi-trophic community (anaerobic and aerobic, culturable and nonculturable Gram +, Gram ? bacteria , actinomycetes, cyanobaceria, protozoa) in a liquid carrier. 33ml/Lwater Total Microbial Activity Microbial activity was assayed by measuring the hydrolysis of fluorescein diacetate (FDA). Two grams of soil (fresh weight, sieved <2 mm) were placed in a 50 ml conical flask, and 10 ml of 60 mM potassium phosphate buffer (pH 7.6) were added. One ml of the FDA stock solution (1mg FDA ml ?1 acetone) was added to start the reaction. Flasks were stoppered and contents shaken by hand. Flasks were then placed in 30 an incubator at 37?C for 45 min. Once removed from the incubator, 10 ml of acetone were added immediately to terminate the reaction. Contents of the conical flasks were then transferred to 50 ml centrifuge tubes and centrifuged at 5000 RPM for approximately 5 min (MSE Scientific Instruments, Coolspin 2 centrifuge). The supernatant from each sample was poured into a vial to measure the optical density at 490 nm on a spectrophotometer (Hitachi U-1100 spectrophotometer). The concentration of fluorescein released during the assay was calculated using a calibration graph produced with standards from 0 to 5 ?g fluorescein ml ?1 which were prepared from a 20 ?g fluorescein ml ?1 standard solution. The 0 ?g ml ?1 fluorescein standard was used to calibrate the spectrophotometer to zero before each set of controls and samples were read (Schnurer and Rosswall, 1982). Plant Growth Measurements Plant growth measurements were performed at the second sampling 28 weeks after transplanting. One strawberry plant was harvested from each of the six replications, and analyzed. Plant material was stored over-night at 4?C, and fresh and dry shoot and root weights (g), chlorophyll content, and growth index (cm 2 ) were recorded the following day. Root architecture was quantified by scanning on custom-made 20 x 20 glass tray using a HP Scan Jet 5370C (Hewlett Packard, Palo Alto, CA) and WhinRHIZO 5.0 (Regent Instruments, Quebec, Canada) computer program. Average root diameter (mm), total root length (cm), surface area (cm 2 ), total root volume (cm 3 ), and number of root tips were obtained using this software. Yield was also recorded from May until June, 2005. 31 Statistical Analysis Data were statistically analyzed according to standard procedures for analysis of variance (GLM) and means separation using least significant difference (LSD) procedures (SAS Institute, Cary, NC). All differences referred to were significant at the 95% confidence level. B. Greenhouse Studies: Greenhouse experiments were conducted at Auburn University in fall 2005, to develop a set of sensitive methods to detect increases in microbial activity following additions of microbial inoculants (PGPR). Seedlings and Soil: Tomato (Lycopersicon esculentum) transplants were produced in the greenhouse at Auburn University. Seeds of tomato hybrid ?Juliet? were planted into 32 cell trays and grown for three weeks using overhead irrigation. Transplants were fertilized weekly with 15-30-15 soluble fertilizer. Soil, collected from the E. V. Smith Agricultural Experimental Station, was autoclaved and mixed with autoclaved sand (3:1 soil: sand) before being placed in 10-inch pots. Seedlings were planted and each pot was inoculated with microorganisms the following day by drenching soil with 100 ml of a stock solution. The stock solution was prepared by diluting the commercial dose of the microbial inoculants in water as shown in Table 1. Experimental Design: The experimental design was a randomized complete block (RCB) with four treatments and three replicates for each sampling time. Treatments were (1) Equity, (2) PGA, (3) AG blend and (4) non-treated control. Samples were taken at 1, 3, 5, 7 and 10 32 days after the first inoculation. Pots were re-inoculated fifteen days later with the stock solution and sampled at the same time intervals. Sampling: At each sampling time three replicates of each treatment were harvested. Roots were shaken to remove loosely attached soil, and the corresponding rizhosphere soil was placed in plastic bags, sealed and brought into the lab for measurement of microbial activity and microbial populations. Fresh and dry shoot and root weights (g), height (cm), number of leaves, chlorophyll content, stem diameter (mm), and growth index (cm 2 ) were also measured as well as the root architecture for every plant. The root architecture was quantified using winRHIZO algorithms. Microbial Activity Total microbial activity was assessed by measuring FDA hydrolysis, dehydrogenase, and arylamidase activity. From each soil sample, procedures were run in triplicate. FDA activity was measured using the procedure suggested by Schnurer and Rosswall (1982). Dehydrogenase activity was assayed on 1g samples of soil. The soil was weighed and placed into test tubes and mixed with 1 ml of TTC (Triphenyl Tetrazolium Chloride) solution. Test tubes were sealed and incubated for 96 hours at 27? C. After incubation, 10 ml of methanol were added to each tube, and the tubes were shaken thoroughly for 30 seconds and then further incubated at room temperature for 3 hours. The soil suspension was then transferred into vials, and the optical density was measured at 485 nm. Standards were made by pouring 0.01 g of TPF (triphenylformazan) into a 100 ml flask with ethanol to obtain concentrations between 0-50 ?g of TPF ml -1 (Runion, personal communication). 33 To evaluate arylamidase activity, one gram of soil from each sample was placed in a 25 ml test tube and treated with 3mL of 0.1 M THAM buffer solution (pH 8.0) and 1mL of 8.0 mM L- leucine ?-naphthylamide hydrochloride. The test tube was swirled for a few seconds to mix the contents and was stoppered and placed on a shaker in an incubator at 37?C for 1 hour. After incubation the reaction was stopped by adding 6 ml of ethanol (95%). The soil suspension was immediately mixed and transferred into a centrifuge tube and centrifuged for 5 minutes at 8000 rpm. The supernatant was transferred to a test tube and a 1 ml aliquot of this supernatant was treated with 1 ml of ethanol, 2 ml of acidified ethanol and 2 ml of ?-dimethylaminocinnamaldehyde. The solution was mixed in a vortex and the intensity of the resulting solution was measured using a spectrophotometer at 540 nm (Hiwada et al, 1977). Microbial Population The Most Probable Number (MPN) technique was used for quantifying microbial populations. One gram of rhizosphere soil was added to 50 ml sterile water in 125 ml Erlenmeyer flasks and shaken for 20 minutes. One ml of the supernatant was placed in a test tube containing Tryptic Soy Broth (TSB) and serial dilutions were made. Test tubes were incubated for 48 hours at 27?C. After incubation, the broth tubes were observed for the presence or absence of growth and the log cfu/g of all treatments were calculated (Garthright, 2001). Statistical Analysis Microbial activity and microbial population values were statistically analyzed according to standard procedures for analysis of variance (GLM), comparison methods (Contrast), correlation, and regression procedures. Root and shoot measurements, as well 34 as root architecture data were analyzed by mean separation (least significant difference) (SAS Institute, Cary, NC). All differences referred to were significant at the 95% confidence level. 3. Results A. Field Studies Total Microbial Activity Soil in the strawberry field test was sampled four times: in December 2004; in April, May and June 2005. Hydrolysis of FDA was significantly different among treatments at the third sampling, but not at other sampling time. At this time microbial activity in AG Blend and Soil Builder treatments were significantly greater than the activity in the nontreated control (Table 2.1). In the last sampling there was a reduction in total microbial activity estimated by FDA; however, the differences among treatments were not significant (Table 2.2). Plant Growth Measurements and Root Architecture The root architecture and other growth parameters assessed from plants in the strawberry field test also showed differences among treatments in the third sampling. Treatments with AG Blend, Soil builder, and Equity had greater growth index values compared to the control (Table 3.1). Relative to the nontreated control treatment with AG Blend significantly increased the average root diameter, surface area, total root volume (cm 3 ), and shoot dry weight. Treatment with Equity significantly increased surface area and dry shoot weight compared to the nontreated control (Tables 3.1 and 3.2). The control had the largest root length value compared to the treatments in which microbial inoculants were applied (Table 3.2). In the last sampling (June 2005), 35 treatments with Equity showed a greater mean root length (246.08 cm) and number of tips (402.83) than did Soil Builder (Table 3.3). During this field test, yield was recorded weekly from May to June. The cumulative yield, expressed as total fruit weight per plot, was significantly greater for Ag Blend than the control (Table 3.4). Treatments with Soil Builder and Equity had no significant difference in yield compared to the control. B. Greenhouse Studies Total Microbial Activity The three procedures used to detect microbial activity after the application of microbial inoculants were measured in both bulk and rhizosphere soil. Bulk soil was considered that which was not attached to the roots. FDA hydrolysis, dehydrogenase and arylamidase were assessed. Results in the bulk soil indicated no difference among treatments at any sampling time. In the rhizosphere soil, FDA hydrolysis consistently was significantly greater than the control at five, seven and ten days after inoculation (DAI) with the PGPR products (Fig. 1). There was no significant difference among the treatments and the control at 1 and 3 DAI. This was true even though the FDA procedure reflected activity due to the fact that values were different than zero. Treatments with Equity (1.33) and PGA (1.40) showed significantly greater activity than the control (0.83) five DAI. All three PGPR products showed an increase in total microbial activity as follows, Equity (0.78), PGA (0.99) AG Blend (0.78) compared to the control (0.29) 7 DAI. FDA procedure also detected changes in the microbial activity due to the application of inoculants at 10 days; however, the magnitude of activity was decreasing, following the same trend across the time from the 3-10 DAI for all the treatments (Fig 1). The commercial products were 36 reapplied at 15 DAI using the previous dose. However, FDA hydrolysis was not sensitive enough to discriminate between treatments (Table 4.1). Overall, FDA hydrolysis was an indicator of microbial activity in the rhizosphere soil, although the procedure was not sensitive enough to detect changes at every sampling time. In the rhizosphere soil, dehydrogenase activity (DHA) detected changes in the microbial activity at 5 DAI only for treatments with PGA (0.62) and AG Blend (0.76), although activity in these treatments was lower than the control (1.04) (Table 4.2). During the test the DHA values were increasing and decreasing without a recognizable pattern for all of the treatments after the first PGPR application. After treatments were re-applied, activity increased 3 DAR-I ( days after re-inoculation), decreased 5 days after re-inoculation (DAR-I) and remained steady through the end, regardless of the procedure?s inability to detect a major variation suitable to the treatments (Table 4.3). In the rhizosphere soil, arylamidase activity was significantly higher than the control at 1 DAI (45.38) and 3 DAR-I (42.70) for Equity treatment (Table 4.3). Microbial activity decreased to 16 at 7 DAI and increased at 10 DAI and 3 DAR-I for all treatments (Fig 3). As for FDA hydrolysis and dehydrogenase activity, values for this procedure varied considerably in a short interval. The fact that even the control was following the same deviation trend as the treatments could be an indicator of the existence of environmental factors that were affecting the response other than PGPR products. Microbial Population Microbial inoculants (PGPR) did not show any significant effect on total population estimated by MPN after the first or second PGPR application (Fig 2). Total population was not different in the rhizosphere compared to the bulk soil. 37 Plant Growth Measurements and Root Architecture PGPR products had effects on plant growth and root architecture; however, they were not consistent through time. As early as 1 DAI, fresh root weight and root tips were significantly higher with PGPR treatments compared to the control (Table 6.1). This increase in number of tips was also found at 3 and 5 DAI and at 5 DAR-I. At 3 DAI, treatment with Equity had an increase in root length and root surface area, while Ag Blend treatment had an increase in dry shoot weight (Table 6.2). Plants and roots sampled at 5 and 7 DAI had higher height, number of leaves, root stem diameter, and dry root weight with Ag Blend (Table 6.3, Table 6.4). Ag Blend maintained this positive response on fresh and dry root weight and root surface area at 10 DAI. Chlorophyll content and growth index were also greater (Table 6.5). At 1 DAR-I, Ag Blend had a positive effect on root stem diameter and root surface area (Table 6.6). The same positive effect of Ag Blend was also observed at 3 DAR-I on number of leaves, chlorophyll content, root stem diameter, and root length (Table 6.7). Roots sampled at 5 DAR-I had higher fresh and dry root weight for Equity and Ag Blend treatments compared to the control (Table 6.8). As shown in the previous sampling at 7 and 10 DAR-I, plants in the Ag Blend treatment also had an increase in root weight and growth index (Table 6.9, Table 6.10). In the last sampling at 15 DAR-I, height of plants was significantly greater for all the PGPR treatments when compared to the control, while chlorophyll content, growth index, and fresh shoot weight were only greater for the Ag Blend treatment (Table 6.11). Analyses of the variables associated with plant growth indicated that the commercial PGPR products were related to important changes in the tomato root architecture differentially throughout the test. 38 4. Discussion Rhizosphere microbial activity involves several interactions among soil, plant and microorganisms. Microbial metabolites (plant growth regulators, phytotoxins, antibiotics, root exudates) and other compounds (enzymes, siderophores, and molecular signals) produced by both microorganisms and plants can also affect the microbial activity in soil (Pinton et al, 2000). Beneficial bacteria that promote plant growth (PGPR) have been widely studied in recent years (Zehnder et al, 2001) showing positive effects on both plant growth and health. These beneficial bacteria, when applied onto the seed or root, colonize and multiply, thereby contributing to the complex ecological interactions in the rhizosphere. Different methodologies related to microbial activity, population and diversity have been suggested to quantify introduced bacteria. In these experiments FDA hydrolysis, dehydrogenase, and arylamidase assays which measure microbial activity were assessed in tomato rhizosphere soil. The results showed that these procedures measured the overall microbial activity, but they did not detect increases in activity consistently across time after PGPR application. FDA hydrolysis has been reported as a promising tool for estimation of biofilms metabolic and physiological activities (Battin, 1997), and for determination of the overall microbial activity in soil (Shnurer and Roswall, 1982). FDA hydrolysis was also sensitive enough to detect increases in activity in the rhizosphere after the application of a nematicide (Fernandez et al, 2001) and for predicting suppressiveness to damping-off in sphagnum peat container media (Inbar et al, 1991). The procedure has also been standardized for use in a wide range of soils (Adam and Duncan, 2000; Green et al, 2006). According to these previous studies, FDA hydrolysis can detect microbial activity 39 in a wide range of environments. However it was not consistently sensitive enough for the purpose of this study. The fluorescein diacetate (FDA) assay depends on the hydrolysis of FDA to yellow-green fluorescent compound by non-specific esterases present in actively metabolizing microbes (Chand et al, 1994; Tsuji et al, 1995). It is generally assumed that FDA diffuses freely into intact cells (Rotman and Papermaster, 1966), where esterases hydrolyze it and the intracellular accumulation of fluorescein results in a useful indicator of cell activity. FDA hydrolysis is a test used only on living cells that convert FDA to fluorescein. Clark et al (2001) concluded that there are other extracts capable of the same conversion in the absence of live cells. It may be possible to find rhizosphere soil compounds that exhibit the same conversion. If that were the case, the FDA procedure would estimate not only the microbial activity but also the contribution from other sources, which could explain the lack of consistency in detecting changes in activity after PGPR application. The microbial activity for the nontreated soil was similar to that where treatments were reapplied. This may indicate several diverse interactions among indigenous and introduced bacterial communities. Soil microbial communities are affected by plant species, soil pH, aeration, and physico-chemical characteristics (Miethling et al 2000; Marschner et al, 2001). Knowing all these key parameters could be relevant in determining correlations among physicochemical and biological indicators and their effect on total microbial activity for treated and nontreated rhizosphere soil. Regarding these interactions, Ownley et al (2003) reported an improvement in the biological control performance of phenazine-producing Pseudomonas fluorescens after the identification of key soil factors. In the same way these key factors also may affect the performance of 40 other PGPR genera, such as Bacillus, and could give us a better understanding of their interaction in the rhizosphere, and how they are influenced by introduced bacteria. Total bacterial populations, estimated by MPN, did not change and did not increase when the procedures used to measure microbial activity were showing changes. A common statistic for indicating the strength of linear relationship existing between microbial activity and bacterial population was used. A negative correlation (-0.7, p=0.03) was only found 7 DAI. This indicates that inoculated microorganisms maintain in general steady levels of total culturable population in the rhizosphere and consequently there is not a correlation with total microbial activity. In this study, with some exceptions, PGPR products exhibited a positive influence on plant growth parameters, root architecture, and yield. Consequently we determined an important correlation in changes in specific root morphological variables due to PGPR. The most consistent response obtained was with Ag Blend. This product contains metabolites of multitrophic bacterial communities, while Equity, Soil Builder, and PGA only contain the bacterial strains. We hypothesized that microbial activity in the rhizosphere increases after the addition of PGPR and it was related to culturable bacterial population. However, this increase was not detected by the procedures used, and based on the results there was not a correlation between activity and population. In conclusion, FDA hydrolysis, dehydrogenase, and arylamidase may serve as indicators of microbial activity, but they were not sensitive enough to detect changes consistently due to PGPR application. FDA was consistent at least in three consecutive samplings after the first inoculation, while neither Arylamidase nor DGH were different than the control in almost all cases. 41 The questions that remain unanswered are the following. First, assuming that total culturable rhizosphere bacteria populations do not change, what changes are caused in the rhizosphere after the application of PGPR? Second, are PGPR really contributing to changes in the overall microbial activity? Although the methods used in this study have advantages and limitations, it may be possible to improve them for monitoring bacterial dynamics in the rhizosphere. References 1. Acosta-Martinez, V., Tabatatai, M.A., 2000.Arylamidase activity of soils. Soil Science Society of America Journal 64(1), 215-221. 2. Adam, G., Duncan, H., 2000. Development of a sensitive and sapid method for the measurement of total microbial activity using fluorescein diacetate FDA in a range of soils. Soil Biology and Biochemistry 33, 943-951. 3. Alef, K.,Nannipieri, P., 1995. Methods in applied soil microbiology and biochemistry. Third edition, Academic press, San Diego, 576 pp. 4. Battin, T., 1997. Assessment of fluorescein diacetate hydrolysis as a measure of total esterase activity in natural stream sediments biofilms. The Science of the Total Environment 198, 51-60. 5. Benitez, E., Melgar, R., Nogales, R., 2004. Estimating soil resilience to a toxic organic waste by measuring enzyme activities. Soil Biology and Biochemistry 36(10), 1615-1623. 6. Clark, J., Gillings, M., Altavilla, N., Beattie A., 2001. Potential problems with fluorescein diacetate assays of cell viability when testing natural products for antimicrobial activity. Journal of Microbiological Methods 46, 261-267. 42 7. Chand, S., Lusunzi, I., Veal, D., Williams, L., Karuso, P., 1994. Rapid screening of the antimicrobial activity of extracts and natural products. Journal of Antibiotics 47, 1295-1304. 8. Doran, J. 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Comparison of human membrane-bound arylamidase neutral arylamidases from small intestine, lung, kidney, liver and placenta. Clinica Chimica Acta 76, 267-275. 14. Hiwada, K. Yokohama, M. Kokubu, T., 1980. Isolation and characterization of membrane-bound arylamidase from human placenta and kidney. Journal of Biochemistry 104, 155-165. 43 15. Inbar, Y., Boehm, M., Hoitnk, H., 1991. Hydrolysis of fluorescein diacetate in sphagnum peat container media for predicting suppressiveness to Damping-off caused by Phytium ultimum. Soil Biology and Biochemistry 23(5), 479-483. 16. Lenhard, G., 1956. The dehydrogenase activity in soils as a measure of the activity of soil microorganisms. Z pflanzenernah Dung Bodenkd 73. 1-11. 17. McCulley, M., 2001. Niches for bacterial endophytes in crop plants: A plant biologist's view. Journal Plant Physiology 28, 983-990. 18. Marschner, P., Yang, C. , Lobeberei, R., Crwley., 2001. Soil and plant specific effects on bacterial community composition in the rhizosphere. Soil Biology and Biochemistry 33, 1437-1445. 19. Miethling, R., Wieland, G., Backhaus H., Tebbe, C., 2000. Variation of microbial rhizosphere communities in response to crop species, soil origin and inoculation with Sinorhizobium meliloti. Microbial Ecology 40, 43-56. 20. Ownley, B., Duffy B., Weller, D., 2003. Identification and manipulation of soil properties to improve the biological control performance of phenazine-producing Pseudomonas fluorescens. Applied and Environmental Microbiology. Biological Control 69(6), 3333-3334. 21. Pinton, R., Nannipieri, P., Varanini, Z., 2000. The rhizosphere: Biochemistry and organic substances at the soil-plant interface, Marcel Dekker, Inc. New York. 22. Rotman, B., Papermaster, B., 1996. Membrane properties of living mammalian cells as studied by enzymatic hydrolysis of fluorescein esters. Proc Natural Academy Society 55, 134-141. 44 23. Schnurer, J., T Rosswall., 1982. Fluorescein diacetate hydrolysis as a measure of total microbial activity in soil liter. Applied Environmental Microbiology 43, 1256-1261. 24. Smith, S.N., Pugh, G.F., 1979. Evaluation of dehydrogenase as a suitable indicator of soil microflora activity. Enzyme and Microbial Technology, 1(4): 279-281. 25. Stevenson, F.J., 1994. Humus Chemistry: Genesis, composition, reactions, 2 nd (Eds.) Wiley New York, 26. Tsuji, T., Kawasaky., Y., Takeshima, S., Seyika, T., Tanaka, S., 1995. A new fluorescein staining assay for visualizing living microorganism in soil. Applied Environmental Microbiology 61, 3415-3421. 27. Zehnnder, G., Murphy, J., Sikora, E., Kloepper, J., 2001. Application of rhizobacteria for induced resistance. European Journal of Plant Pathology 107, 39-50. 45 Table 2.1 Effect of commercial PGPR products on fluorescein diacetate hydrolysis (FDA) as a measure of total microbial activity in strawberry rhizosphere soil (May 2005) Means followed by the same letter are not significantly different. *Represents ?g fluorescein/g dry soil per hour Treatment Commercial PGPR products FDA* 1. AG Blend? 2.46 a 2. Soil Builder? 2.50 a 3. Equity? 2.40 ab 4. Control 1.96 b LSD 0.05 0.4942 46 Table 2.2 Effect of commercial PGPR products on fluorescein diacetate hydrolysis (FDA) as a measure of total microbial activity in strawberry rhizosphere soil (June 2005) Treatment Commercial PGPR products FDA* 1. AG Blend? 0.8276 a 2. Soil Builder? 0.8856 a 3. Equity? 0.7583 a 4.Control 0.7676 a LSD 0.05 0.361 Means followed by the same letter are not significantly different. * Represents ?g fluorescein/g dry soil per hour 47 Table 3.1 Effect of Commercial PGPR products on strawberry plant growth (May 2005) Treatment Root fresh weight (g) Shoot dry weight (g) Growth index* (cm 2 ) 1. AG Blend? 11.318 a 53.350 a 1074.64 a 2.Soil Builder? 11.140 a 46.817 ab 1058.55 a 3.Equity? 12.294 a 60.549 a 1024.39 a 4.Control 10.179 a 37.517 b 894.65 b LSD 0.05 4.743 13.744 108.03 Means followed by the same letter within a column are not significantly different. * Growth index was calculated as height (cm) x width (cm). 48 Table 3.2 Effect of Commercial PGPR products on strawberry root architecture* (May 2005) Treatment Total root length (cm) Root surface area (cm 2 ) Root volume (cm 3 ) Mean root diameter (mm) Number of root tips 1.AG Blend? 139.13 b 304.74 a 80.12 a 9.292 a 296.50 a 2.Soil Builder? 139.72 b 269.68 ab 51.01 ab 7.380 ab 302 a 3. Equity? 137.342 ab 323.87 a 61.02 ab 7.713 ab 365.50 a 4.Control 218.06 a 233.78 b 20.45 b 3.278 b 310.17 a LSD 0.05 76.983 67.803 53.732 5.7612 120.02 Means followed by the same letter are not significantly different. * Root architecture parameters were assessed with Whinrhizo 5.0 computer ? ? 49 Table 3.3 Effect of Commercial PGPR products on strawberry root architecture* (June 2005) Treatment Total root length (cm) Root surface area (cm 2 ) Root volume (cm 3 ) Mean root diameter (mm) Number of root tips 1.AG Blend? 222.24 ab 343.68 a 45.47 a 5.03 a 361.17 ab 2.Soil Builder? 175.62 b 306.63 a 45.42 a 5.50 a 311.02 b 3. Equity? 246.08 a 297.14 a 33.27 a 4.12 a 402.83 a 4.Control 193.44 ab 344.75 b 60.52 a 6.33 a 326.00 a LSD 0.05 63.034 105.31 36.59 2.67 120.02 Means followed by the same letter are not significantly different. * Root architecture parameters were assessed with Whinrhizo 5.0 computer 50 Table 3.4 Effect of Commercial PGPR products on strawberry yield Treatment Yield (g) 1. AG Blend? 11275 a 2.Soil Builder? 8814 b 3.Equity? 9580 b 4.Control 8940 b LSD 0.05 2415 Means followed by the same letter are not significantly different. 51 Table 4.1 Effects of commercial (PGPR) products on fluorescein diacetate hydrolysis as a measure of total microbial activity in tomato rhizosphere soil FDA Treatment Days After Re-Inoculation 1 3 5 7 10 15 1. Equity? 0.85 1.30 0.73 0.47 1.64 0.65 2. PGA? 1.17 0.99 0.69 0.50 0.96 0.90 3. Ag Blend? 0.94 0.98 0.70 0.68 1.12 0.75 4. Control 1.10 0.97 0.69 0.61 0.78 0.80 * Indicates significant difference from the control Fluorescein diacetate (FDA) hydrolysis in ?g fluorescein /h g of dry soil 52 Table 4.2 Effect of commercial PGPR products on total microbial activity measured by dehydrogenase activity in tomato rhizosphere soil Dehydrogenase Activity Treatment Days After Inoculation 1 3 5 7 10 1. Equity? 1.18 1.20 0.98 1.04 1.39 2. PGA? 0.75 1.22 0.62* 0.98 1.48 3. Ag Blend? 0.70 0.62 0.76* 1.61 1.83 4. Control 1.27 0.95 1.04 2.53 1.57 * within a column indicates significant difference from the control Dehydrogenase nmol triphenilformazan-TPF/ g dry soil h 53 Table 4.3 Effect of commercial PGPR products on total microbial activity measured by dehydrogenase activity in the tomato rhizosphere soil Treatment Days After Inoculation 1 3 5 7 10 1. Equity? 1.22 1.79 0.25 0.23 0.29 2. PGA? 0.41 0.66 0.32 0.29 0.04 3. Ag Blend? 0.39 0.76 0.34 0.29 0.04 4. Control 0.36 0.65 0.24 0.23 0.12 * Indicates significant difference from the control Dehydrogenase nmol triphenilformazan-TPF/ g dry soil h 54 Table 4.4 Effect of commercial PGPR products on microbial activity measured by Arylamidase activity in the tomato rhizosphere soil Treatment Days After Inoculation Days After Re-Inoculation 1 3 5 7 10 1 3 5 7 10 1. Equity 45.38* 33.02 25.73 16.75 32.29 19.25 42.7* 14.82 14.47 12.90 2. PGA 23.28 25.39 22.05 17.07 40.85 19.83 33.65 19.25 17.67 12.48 3. Ag Blend 23.73 23.16 18.93 14.95 39.67 21.80 30.69 16.60 17.47 16.63 4. Control 20.93 27.32 23.24 16.03 36.01 24.59 34.57 18.66 23.40 16.05 * Indicates significant difference from the control Arylamidase activity in nmol x 10 2 ?-naphthylamine /g dry soil h Table 5.1 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements one day after inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 12.33a 5.30a 27.96a 3.37a 2.38a 0.363a 4.67a 0.051a 99.02a 60.85a 2.97a 1.93a 432.0a 2. PGA 11.67a 4.67a 28.76a 3.55a 2.67a 0.294a 4.95a 0.061a 96.74a 58.14a 2.81a 1.92a 361.3a 3. Ag Blend 13.00a 5.00a 28.93a 3.53a 3.18a 0.344a 4.71a 0.240a 90.31a 53.76a 2.57a 1.90a 355.6a 4. Control 12.13a 4.67a 28.73a 3.37a 2.30a 0.345a 4.37b 0.046a 84.28a 50.75a 2.47a 1.89a 263.0b LSD 0.05 2.06 0.94 2.3 0.623 1.02 0.182 0.214 0.24 20.65 18.2 1.35 0.45 79.9 Means followed by the same letter within a column are not significantly different. H: Height L: Length NL: Number of leaves SA: Surface area CH: Chlorophyll V: Volume SD: Stem diameter D: Root diameter SFW: Shoot fresh weight NT: Number of tips SDW: Shoot dry weight RFW: Root fresh weight RDW: Root dry weight 55 Table 5.2 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements three days after inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 13.16a 6.3a 33.2a 3.81a 3.10a 0.55b 6.7ab 0.253a 146.3a 99.52a 5.46a 2.16a 732.0a 2. PGA 12.83a 6.0a 34.0a 3.72a 3.08a 0.60b 5.6b 0.077a 115.3ab 92.56ab 6.25a 2.56a 533.0a 3. Ag Blend 11.83a 6.0a 35.4a 3.95a 3.72a 0.91a 7.2a 0.058a 115.7ab 79.71ab 4.44a 2.21a 503.6a 4. Control 12.16a 6.3a 34.7a 4.42a 3.64a 0.56b 6.7ab 0.063a 97.96b 58.65b 2.47a 1.91a 361.3c LSD 0.05 3.37 1.21 2.94 0.84 1.9 0.13 1.38 0.291 33.44 37.56 3.74 0.785 141.67 Means followed by the same letter are not significantly different. 56 Table 5.3 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements five days after inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 18.0b 7ab 34.47ab 4.9ab 6.73b 5.11a 0.160a 96.02a 148.15a 11.3a 3.00a 717.7a 2. PGA 15.6c 5c 33.46b 4.7b 5.46b 5.42a 0.085b 104.75a 100.80a 7.8a 2.92a 731.3a 3. Ag Blend 20.6a 7a 35.66a 5.5a 11.47a 5.09a 0.180a 126.49a 128.45a 10.5a 3.21a 534.0a 4. Control 18.0b 6bc 33.90ab 4.7b 6.30b 4.88a 0.146ab 99.57a 101.28a 8.3a 3.26a 333.3c LSD 0.05 1.215 1.08 2.15 0.76 1.71 0.658 0.062 70.7 53.52 5.44 0.87 101 Means followed by the same letter are not significantly different. 57 Table 5.4 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements seven days after inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 19.16b 8.3b 38.3b 4.86a 10.31c 1.3b 2.9a 0.82a 185.48a 155.48a 11.48a 2.85a 448.67a 2. PGA 19.16b 8.3b 37.3b 5.71a 11.49b 1.4b 2.8a 0.45a 182.25a 166.87a 12.55a 3.04a 431.67a 3. Ag Blend 23.33a 9.6a 41.0a 5.67a 17.57a 1.9a 2.2a 0.42a 139.60a 160.84a 14.86a 3.66a 363.33a 4. Control 19.33b 9.0ab 37.4b 4.98a 10.23c 1.3b 2.2a 0.26a 130.90a 166.91a 18.65a 4.44a 359.00a LSD 0.05 2.22 0.94 2.35 1.16 1 0.26 0.92 1.05 76.5 29.34 9.78 2.04 114.38 Means followed by the same letter are not significantly different. 58 Table 5.5 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements ten days after inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) GI (cm 2 ) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 56a 10.3b 37.3a 6.70a 2260ab 39.67a 8.4a 4.92a 0.348a 224.57a 306.7a 34.93a 4.57a 547a 2. PGA 56a 11.3ab 37.2b 7.06a 2682a 36.48a 13.2a 2.75c 0.331a 218.28a 252.2b 26.27ab 4.11a 474a 3. Ag Blend 54a 13.3a 43.0a 7.67a 2642a 46.42a 13.7a 3.62b 0.401a 190.93a 224.4c 21.04b 3.62a 474a 4. Control 51a 11.3ab 38.1b 7.43a 1868b 40.90a 9.8a 2.40c 0.212b 174.25a 177.7c 14.37b 3.22a 395a LSD 0.05 5.35 2.17 3.1 1.66 530.75 10.72 6 0.83 0.1 101.27 15.6 12.26 1.92 169.2 Means followed by the same letter are not significantly different. 59 Table 5.6 Effect of Microbial Inoculants (PGPR) on root architecture related variables and plant growth measurements one day after re-inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) GI (cm 2 ) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 42.0a 10.33a 41.4a 7.7a 2540a 39.57a 8.43a 10.55a 0.91a 191.2a 494.9ab 154.4a 12.6a 572.3a 2. PGA 43.0a 11.66a 39.9a 6.8bc 2580a 36.48a 13.16a 12.24a 0.92a 148.6a 507.5ab 145.2a 11.2a 381.3a 3. Ag Blend 47.6a 11.66a 41.5a 7.2ab 2156a 46.42a 13.76a 10.26a 1.01a 191.9a 587.4a 133.5a 9.4a 452.0a 4. Control 50.3a 10.33a 36.4a 3.5c 1974a 40.90a 9.86a 9.98a 0.69a 173.2a 441.2b 97.0a 8.9a 533.3a LSD 0.05 8.66 1.95 9.8 0.63 913.88 10.7 6 3.36 0.56 57.96 92.6 70.55 5.2 235.93 Means followed by the same letter are not significantly different. 60 Table 5.7 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements three days after re-inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) GI (cm 2 ) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 49.0a 11.0ab 35.5b 6.37b 1910.3a 31.8b 6.86a 11.54a 1.10a 214.7ab 480.0ab 88.6a 7.4ab 593.3a 2. PGA 53.7a 10.0b 34.8b 6.93b 2813a 44.3ab 6.13a 12.24a 1.34a 282.0a 370.3b 63.2a 5.4b 669.7a 3. Ag Blend 50.3a 12.3a 46.6a 7.85a 2674a 50.1a 7.96a 12.68a 1.37a 173.6b 449.7ab 92.8a 8.3ab 727.0a 4. Control 55.0a 10.3ab 36.7b 6.38b 2018a 44.1ab 7.03a 13.02a 1.16a 173.5b 535.4a 84.7a 10.8a 548.0a LSD 0.05 7.74 2.24 3.61 0.88 917.33 12.84 3.11 3.57 0.6 90.8 153.01 30.5 4.2 243.55 Means followed by the same letter are not significantly different. 61 Table 5.8 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements five days after re-inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) GI (cm 2 ) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 54.00a 11.33a 41.0ab 7.13a 2823a 46.26a 14.7a 15.93a 2.95a 259.62a 535.43a 86.45a 6.56a 925.7a 2. PGA 46.67b 10.66a 33.3b 7.08a 2373a 38.66a 11.4a 14.19ab 1.66b 257.60a 517.44a 85.80a 6.76a 760.3b 3. Ag Blend 47.67b 12.33a 43.9a 7.20a 2553a 52.66a 12.2a 16.96a 3.22a 155.69a 493.13a 125.75a 10.37a 545.0c 4. Control 55.67a 10.67a 38.3ab 6.33a 2589a 43.06a 13.9a 12.00b 1.63b 177.82a 516.97a 129.70a 10.00a 601.6c LSD 0.05 4.21 1.95 8.36 1.5 748.5 14.08 5.41 3.6 1.2 113.96 84.84 54.67 4.37 136.55 Means followed by the same letter are not significantly different. 62 Table 5.9 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements seven days after re-inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) GI (cm 2 ) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 48.0b 12a 33.7b 6.38c 1891a 31.11b 7.5b 7.83b 1.03b 188.23ab 473.88a 102.98a 9.0b 481.3a 2. PGA 53.3a 12a 35.9b 7.06ab 2373a 36.16ab 15.8a 9.06ab 1.13b 203.62a 481.73a 102.26b 8.2b 486.0a 3. Ag Blend 51.6ab 13a 48.9a 7.59a 2042a 46.28a 15.6a 12.67a 1.96a 73.53b 534.37a 319.06a 23.9a 464.6a 4. Control 49.3ab 11a 35.4b 6.76bc 1854a 33.17ab 10.7ab 9.56ab 1.6ab 187.37ab 494.73a 124.00b 9.6b 545.6a LSD 0.05 4.51 1.71 5.92 0.6 860.7 11.1 7.4 4.72 0.73 124.15 114.52 110.75 8.5 222 Means followed by the same letter are not significantly different. 63 Table 5.10 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements ten days after re-inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) GI (cm 2 ) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 56a 10.33b 37.36b 6.7a 2261ab 39.32b 11.06b 1.06b 1.53b 76.17a 470.57a 274.17a 23.26a 294.67b 2. PGA 56a 11.33ab 37.20b 7.1a 2682a 39.67b 9.06bc 10.70b 3.43ab 114.71a 470.61a 144.4ab 12.81ab 448.33ab 3. Ag Blend 54a 13.33a 43.06a 7.7a 2661a 60.51a 18.73a 17.86a 6.06a 79.78a 519.67a 200.17ab 13.59ab 430.00b 4. Control 51a 11.33ab 38.06b 7.4a 1868b 36.20b 6.46c 11.06a 1.93b 93.58a 489.51a 96.40b 7.96b 601.33a LSD 0.05 5.35 2.17 3.06 1.67 444.7 10.38 2.66 2.4 2.87 98.8 78.07 140.87 12.91 169.94 Means followed by the same letter are not significantly different. 64 Table 5.11 Effect of microbial inoculants (PGPR) on root architecture related variables and plant growth measurements fifteen days after re-inoculation Treatment SHOOT MEASUREMENTS ROOT MEASUREMENTS H (cm) NL CH SD (mm) GI (cm 2 ) SFW (g) SFW (g) RFW (g) RFW (g) L (cm) SA (cm 2 ) V (cm 3 ) D (mm) NT 1. Equity 59.33a 11.7bc 34.3bc 7.54a 2286b 51.4b 25.0a 13.03b 2.3a 154.34a 368.9a 423.5a 12.65a 517.7a 2. PGA 60.66a 11.0c 36.0b 7.55a 2118b 47.9b 16.8a 11.86b 2.7a 182.90a 496.3a 144.9a 11.53a 602.3a 3. Ag Blend 60.66a 14.7ab 40.0a 8.13a 3020a 65.4a 24.9a 17.26a 4.2a 178.25a 550.2a 148.5a 10.80a 536.0a 4. Control 51.33b 15.3a 33.2c 7.46a 2196b 39.9b 16.4a 14.36ab 1.7a 11.18a 548.7a 233.8a 16.75a 400.0a LSD 0.05 6.67 3.52 2.4 1.42 488.19 13.93 11.24 4.23 2.7 154.42 193.43 427.9 12.34 297.37 Means followed by the same letter are not significantly different. 65 66 Fluorescein Diacetate Hydrolysis 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 5710 time (days after incoulation) f l u o r escei n / g so i l Equity PGA AgBlend Control * * * * * ** * FIGURE 1 Effect of commercial PGPR products on total microbial activity measured by FDA hydrolysis in the tomato rhizosphere soil 67 FIGURE 2 Effect of commercial PGPR products on culturable bacterial populations in the tomato rhizosphere Bacterial Population-MPN 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 135710 time (days) lo g cf u / g Equity PGA AgBlend Control 68 EFFECTS OF MICROBIAL INOCULANTS ON SOIL MICROBIAL POPULATIONS, DIISEASE SUPPRESSIVENESS, AND SOIL HEALTH 1. Introduction Soil suppressiveness is the result of factors that constrain pathogen establishment, persistence, or increase in population (Pyrowolakis et al, 2002). Therefore, under certain conditions, the pathogenicity of fungi, nematodes, or bacteria is expressed, and the susceptible plants remain unharmed even under environmental conditions favorable to the expression of disease. Soil suppressiveness may be general or specific (Cook and Baker, 1983). General suppressiveness of a pathogen is directly related to the biological, physical, and chemical environment. Specific suppressiveness operates against a select group of microorganisms. Thus, the soil initially allows disease development, but after repeated cultivation of the host plant in the same field, the soil progressively becomes suppressive (Davet, 2004). Take-all disease, caused by Gaeumannomyces graminis var. tritici (Ggt), is common in wheat-producing areas. A phenomenon called take-all- decline, which describes a reduction in the severity of the disease after a few years of wheat monoculture, results from specific suppression (Weller and Thomashow, 2003). General soil suppressiveness against plant parasitic nematodes can be the result of physical factors, such as soil type, moisture, and temperature. Antagonists such as egg- parasitic fungi, nematode-trapping fungi, bacteria, and polyphagous predatory nematodes 69 also limit nematode abundance and may be used for inducing suppressiveness in the soil to reduce nematode populations (Gray, 1995; Kerry, 1998). Most nematodes are free-living and sustain themselves by feeding on bacteria or other microorganisms (Barker and Koenning, 1998). Other species are parasites of plants and animals. The root knot nematode (Meloidogyne sp.) is a biotrophic pathogen of numerous plant species (Williamson and Gleason, 2003). This organism causes important changes in the morphology and physiology of its host. Juveniles of the root- knot nematode move intercellularly after penetrating the root, migrating down towards the root tip where they enter the base of the vascular cylinder and migrate up the root (Wyss et al., 1992). The juveniles establish a permanent feeding site in the differentiation zone of the root by inducing nuclear division without cytokinesis in host cells. The plant cells around the feeding site divide and swell, causing formation of galls (Williamson and Hussey, 1996). Root knot nematodes are economically important for losses they cause on many crops world wide (Sikora and Fernandez, 2005). There are several control alternatives that include the use of chemicals. Recently, attempts have been made to use antagonistic fungi and to identify root-knot nematode suppressive soils (Pyrowolakis, et al. 2001; Kerry and Hidalgo-Diaz, 2004; Nico et al, 2004). Lara et al. (1996) demonstrated that Paecilomyces lilacinus significantly reduced Meloidogyne incognita soil populations and increased tomato yield. Fernandez et al. (2001) studied the effect of the nematicide Ditera on the induction of soil suppressiveness to the root-nematode. Changes in the rhizosphere bacterial community were found as well as enhancement of antagonism to the nematode. 70 Pyrowolakis et al. (2002) rated 12 California soils for suppressiveness to M. incognita by comparing the nematode population development in fumigated and re- infested soils to the equivalent non-treated soils. In three of 12 soils the population density development of the nematode was significantly suppressed compared to methyl iodide-fumigated soil. However, infestation timing or life stage of M. incognita did not influence soil suppressiveness against the nematode. Kokalis-Burelle and Kloepper (2004) discussed soil ecosystem heath and its role in plant disease suppression. Practices such as crop rotation, cover crops, organic amendments, and application of a biological agent modified the rhizosphere microbial community and led to enhanced plant health ( Schippers et al., 1987; Weller et al., 2002). Such changes in soil microbial communities via antagonism are related to soil suppressiveness. Understanding soil microbial community interactions is fundamental for developing practices to manage plant diseases. The knowledge that agricultural production depends on complex biological equilibria will aid in modifying agro- ecosystems and obtaining more favorable conditions for plant growth and health. The goal of this project was to determine the relationship among soil microbial activity, microbial population, and disease suppressiveness after the addition of soil inoculants PGPR (plant growth promoting rhizobacteria). 2. Materials and Methods Two greenhouse studies were conducted at Auburn University in 2005 and 2006. For both studies tomato transplants ?hybrid Juliet? were produced in the greenhouse at Auburn University. Transplants were fertilized weekly with Miracle Gro 15-30-15 (N-P- 71 K fertilizer, Scotts Miracle GRO) soluble fertilizer and transplanted 3 weeks after seeding. Soil, collected from the E. V. Smith Agricultural Experimental Station, was autoclaved twice (90 minutes each cycle at 117 ?C) before being placed in pots. A stock solution of each microbial treatment was prepared by diluting the commercial dose of the microbial inoculants in water (Table 1). Seedlings were planted and each pot was inoculated the following day by pouring 100 ml of microbial stock solution onto the soil surface. A. Trial 1 This experiment was designed to test the hypothesis that microbial activity is related to population size. Fluorescein diacetate hydrolysis was used to quantify microbial activity. Direct plate counting was used to assess culturable bacterial population. Experimental Design The experimental design was completely randomized with five treatments and six replications. Treatments were (1) Equity ( Naturize Inc, Jacksonville, FL) (2) PGA (Organica, Norristown, PE), (3) Ag Blend (Superbio, Pilot Point, TX), (4) FZB42 (Abitep, Berlin, Germany), and (5) nontreated control. Treatments 1 to 4 are commercial PGPR products discussed in Table 1. Samples were taken at 1, 5, 10, and 15 days after the inoculation with PGPR commercial products for microbial activity, population size and growth measurements. This experiment was conducted twice. For the second experiment only Bioyield and FZB42 were used. Sampling was done at 1, 4, 8, 12, and 16 days after inoculation for microbial activity and population size measurements. 72 Table 1. Commercial PGPR Products Microbial Inoculants (PGPR) Description Dose Naturize Equity? Contains 47 strains of bacilli in a liquid formulation. 0.48g/L water BioYield? Contains strain B. subtilis GB03 and Bacillus amyloliquefaciens strain GB99 in a chitosan carrier. 1cc product: 40 cc soil Organica Plant Growth Activator (PGA) ? Contains 54 strains of bacilli, Pseudomonads, Actinomycetes, and Trichoderma in a powder carrier. 1 tablespoon/Gal water Ag Blend? Contains multi-trophic community (anaerobic and aerobic, culturable and nonculturable Gram +, Gram ? bacteria , actinomycetes, cyanobaceria, protozoa) in a liquid carrier. 33ml/Lwater FZB42? Contains strain Bacillus amyloliquefaciens in a liquid formulation. 10 6 cfu/ml water Microbial Activity and Population Total microbial activity was assessed by FDA hydrolysis, using the procedure suggested by Schnurer and Rosswall (1982) for the first experiment. For the second experiment, FDA hydrolysis was determined by a modified procedure described by Green et al. (2006), where 1 g of air-dried soil and 50 ml of 60 mM sodium phosphate buffer (pH 7.6) were placed in a 125 ml Erlenmeyer flask. Then 0.50 ml of 4.9 mM FDA 73 lipase substrate solution was added, suspension was shaken and placed in an incubator for 3h at 37 ?C. The reaction was stopped by the addition of 2 ml of acetone. About 30 ml of the suspension were transferred to a 50 ml centrifuge tube and centrifuged at 8000 rpm for 5 minutes. The supernatant was filtered through a Whatman No. 2 filter paper and the filtrate transferred to a colorimeter tube to measure the absorbance at 490 nm. The concentration of fluorescein released was expressed in mg/ dry soil x 3h. Direct plate counts were used to quantify total culturable bacteria, heat tolerant bacteria, and fluorescent pseudomonads. One gram of rhizosphere soil was added to 50 ml sterile water in 125 ml Erlenmeyer flask and shaken at 150 rpm for 20 minutes. Serial dilutions were made, and 50 ?l were plated onto 50% Tryptic Soy Agar (TSA) for total bacteria (10 -1 , 10 -2 and, 10 -3 dilutions) and onto King?s B media (king et al., 1954) for fluorescent pseudomonads (10 -1 , 10 -2 dilution). For total heat tolerant bacteria after serial dilution, cell suspensions were heat-treated for 13 minutes at 80 ?C and then 10 -1 and 10 -2 dilution were plated onto 50 % TSA. All plates were incubated 48 hours at 28 ?C. Numbers of colonies were counted, and population size expressed as log cfu/g of soil for all treatments. Plant Growth Parameters and Root Architecture Plant measurements and root architecture analysis were performed at every sampling time for the first experiment. Fresh and dry shoot and root weight (g), growth index (height x wide (cm 2 )), and height (cm) were measured. WinRHIZO (Regent Instruments, Quebec Canada) algorithms were used to quantify tomato root architecture. 74 Statistical Analysis Microbial activity and population data were statistically analyzed according to standard procedures for analysis of variance and by mean separation, least significant difference (LSD) (SAS Institute, Cary, NC). Root architecture and plant measurements values were also analyzed using LSD. All differences referred to were significant at the 95% confidence level. B. Trial 2 This experiment was designed to test the hypothesis that microbial inoculants can be used to induce suppressiveness to soilborne pathogens by maintaining adequate microbial population and activity. Soil suppressiveness was studied using the root knot nematode Meloidogyne incognita, and tomato as a model. Proven Nematode Pathogenicity To prove pathogenicity on tomato, three-week-old tomato seedlings and inoculum of M. inocgnita race 3 were used. Inoculum of M. inocgnita was prepared by extracting nematode eggs from cotton roots, obtaining a solution with 1000 eggs/ml of water. Seedlings were placed in pots previously filled with autoclaved soil and 5 ml of the inoculum solution applied per pot as a drench after transplanting. Forty five days after inoculation the experiment was harvested and pathogenicity was confirmed on tomato. Nematodes eggs were extracted from tomato roots and used as inoculum for each trial. Nematode Inoculum Preparation This trial was conducted four times. For each time nematode eggs were extracted from tomato roots of the previous trial, and a nematode inoculum solution was prepared and applied as a drench one day after PGPR application. 75 Experimental Design The experimental design was completely randomized with four treatments and 8 replicates. The trial was conducted four times. For the first and second time four treatments were used: (1) Equity (2) BioYield (3) Ag Blend (4), and Control. For the third and fourth times, Ag Blend treatment was replaced by FZB42 (Table 1). Sampling: The experiment lasted for 45 days. At the end of this time, a rhizosphere soil sample was taken for microbial activity and population determinations. Root systems were then rated for the nematode-induced galling on a scale of 0 to 6 as follows: 0= no galls, 1 = 1-10%, 2= 11-25%, 3= 26-50%, 4=51-75%, 5=69-90%, and 6= 91-100% roots with galls (Kathy Lawrence, personal communication). Fresh and dry shoot and root weights (g) were also recorded for each plant. Eggs were recovered from excised roots by agitated extraction in a 10% bleach solution (1.5% sodium hypochlorite, NaOCl). The extracted solution was poured through a sieve with a pore size of 73.7 ?m nested over a sieve with a pore size of 25.4 ?m in which the eggs were collected. Eggs were rinsed gently with running water and transferred into a vial with 10 ml of water (Klump and Thomas, 1987). The total number of eggs were counted under a dissecting scope (4X) and expressed as number of eggs per gram of root. The extraction of juveniles from the soil was completed by direct soil screening followed by sucrose centrifuge flotation. Direct soil screening technique consisted of washing the content of every pot separately through a 40-mesh (425 ?m) opening, screening with tap water to remove larges pieces of debris, and collecting liquid in a bucket. The soil suspension was then mixed by hand and allowed to settle for 45 76 seconds. The suspension was poured through a 325-mesh sieve to collect nematodes. The contents of the sieve were rinsed with a wash bottle into a beaker. The same procedure was repeated twice. Juveniles were extracted, counted under the dissecting scope, and expressed as juveniles per ml. Microbial Activity and Population Total microbial activity was assessed by measuring FDA hydrolysis, using the procedure suggested by Schnurer and Rosswall (1982). Direct plate count technique was used for measuring bacterial population as described in trial 1. Statistical Analysis Data collected were statistically analyzed according to standard procedures for analysis of variance (GLM) and mean separation using least significant difference LSD (SAS Institute, Cary, NC). All differences referred to were significant at the 95% confidence level. 3. Results A. Trial 1 Total Microbial Activity and Population In the tomato rhizosphere soil, total microbial activity measured by FDA hydrolysis was significantly higher than the control only for Equity treatment at 10 days after inoculation (DAI) (Table 2.1). Treatment with Equity resulted in significantly higher populations of total bacteria and fluorescent pseudomonads than the control at 1 DAI. In contrast, FZB42 treatment resulted in greater populations of total bacterial and fluorescent pseudomonads at all 77 times, with the exception of 1 DAI (Table 2.2). FZB42 treatment also caused a significant increase in total heat tolerant bacteria at all sampling times. This experiment was conducted a second time in which only FZB42 and Bioyield PGPR products were applied. FDA hydrolysis, determined by a modified procedure (Green et al, 2006), detected significant increases in total microbial activity for FZB42 treatment compared to the control at 4 and 12 DAI (Table 2.3). The same increase in total microbial activity was obtained with Bioyield treatment at 4, 8 and 12 DAI (Table 2.4). In the same experiment total bacterial populations were significantly greater than the control with FZB42 treatment at the first and last sampling (1 and 16 DAI); total heat- tolerant bacteria were greater at 1 and 8 DAI; and fluorescent pseudomonads were greater at 12 DAI (Table 2.5). Bioyield treatment also resulted in increases in populations of total bacteria. Thus, populations were greater than the control at 1 and 8 DAI; however, populations of fluorescent pseudomonads were not different than the control at any sampling time (Table 2.6). Plant Growth Parameters and Root Architecture PGPR products had positive effects on plant growth parameters and root architecture. PGA treatment increased shoot dry weight (SDW) at 1 DAI (Table 3.2). Ag Blend treatment caused significantly higher shoot fresh weight, SDW, and growth index (GI) than the control at 10 and 15 DAI (Table 3.6). Additionally, root fresh and dry weights were increased at 15 DAI (Table 3.8). Two variables of root architecture were higher than the control for Equity and Ag Blend treatments. Increases in number of root tips were detected 10 DAI for Equity treatment (Table 3.5) and in root diameter for Ag Blend at 15 DAI (Table 3.7). 78 Five days after microbial inoculation significant effects on root architecture related variables were not observed for any PGPR treatment (Table 3.3). Additionally, plant growth measurements such as SFW, SDW, GI and Height were not significant different for Ag Blend treatment than the control; however, those growth parameters for PGA, Equity and FZB42 treatment were significantly lower than the control (Table 3.4). Ten DAI FZB42 treatment had also the lowest root length (Table 3.5). B. Trial 2 Population of M. incognita on tomato rhizosphere soil inoculated with PGPR products and effects on root growth Bioyield-treated tomato plants had a significantly lower mean gall rating and numbers of eggs/g and juveniles than the control (Table 4.1). No other PGPR treatment had a significant effect on all three parameters. Fresh root weight was significantly higher for Bioyield and Ag Blend treatments than the control (Table 4.1). For the third and fourth times this experiment was conducted, Ag Blend treatment was replaced by FZB42. Bioyield treatment had similar results on the development of nematode populations (Table 4.2) as described above, as did FZB42 treatment (Table 4.3). Reductions in gall rating, egg/g, and juveniles were observed for both treatments. Equity treatment again had a significant reduction in gall rating and number of juveniles per ml (Table 4.3). Shoot dry weight was significantly higher in all PGPR treatments than the control (Table 4.2) Total Microbial Activity and Population Applications of microbial inoculants (PGPR) resulted in some significant effects on population size measured by direct plate counts. Populations of total bacteria and 79 total heat-tolerant bacteria were significantly higher in Bioyield and FZB42 treatments than the control (Table 5.1, Table 5.2). However, increases in microbial activity assessed by FDA hydrolysis were not detected (Table 5.1). 4. Discussion Plant growth-promoting rhizobacteria (PGPR) are plant associated microorganisms that benefit plant growth and health. Stimulation of plant growth includes a variety of mechanisms that provide the plant with fixed nitrogen, phytohormones, iron and soluble phosphate (Glick et al, 1999). PGPR also compete with pathogens that inhibit plant growth and development (Glick and Bashan, 1997). The results presented here indicate that different treatments of commercially available PGPR-based inoculants (Equity, PGA, Ag Blend, Bioyield, and FZB42) generally exerted positive effects on tomato growth. Additionally, under our experimental conditions, application of Bioyield and FZB42 at transplanting time contributed to the suppression of root-knot nematode, consistently reducing M. incognita population and root damage. Under our experimental condition a phosphorous deficiency was detected in the first trial. This deficiency could explain the negative effects of the application of PGPR products on tomato growth parameters at five days after inoculation because the plants were under obvious stress conditions, their physiological state would be altered, possibly allowing PGPR to exert an unexpected negative effect. When comparing populations of nematodes in PGPR-treated soils with nontreated soils, reductions were observed for gall rates, egg/g, and juveniles/ml. Bioyield is a product that contains B. subtilis strain GBO3 for control of soilborne pathogens via 80 production of antibiotics, chitosan for nematode control via promotion of indigenous soil predators and antagonists to root-knot nematodes, and a strain of Bacillus amyloliquefaciens that elicits induced systemic resistance (Kloepper et al, 2004). Consequently, results obtained in this study are another example of effectiveness of this PGPR product against root-knot nematode and also of the promotion of free-living nematodes (data not reported). Bacillus amyloliquefaciens strain FZB42 can be distinguished in this study by several important features for rhizosphere competence and for suppression of soil-borne pathogens such as root-knot nematode. It has been reported that FZB42 is a producer of lipopeptides, surfactins, bacillomycins D, and fengycins, which are secondary metabolites with mainly antifungal activity (Chen et al, 2006). This antibiotic production not only could have antifungal and antibacterial effects but also may influence M. incognita population development by reducing the number of second stage juveniles. The rhizosphere is known to be a zone of increased microbial activity and consequently enzyme activity. Because of the intensive and extensive interactions in the rhizosphere, we used microbial activity and population size to assess the positive effects of inoculated microorganisms and to determine frequency of PGPR applications. FDA hydrolysis is an important enzymatic assay; it is simple, sensitive, and precise in measuring soil microbial activity. Green et al. (2006) optimized the FDA hydrolysis procedure first suggested by Schnurer and Rosswall (1982) to measure soil microbial activity. They found that this modified procedure provided a better quantification of microbial activity when it was tested in soils with a wide range in pH, organic C and texture (Green et al., 2006). This recently optimized FDA procedure was 81 also used in our studies and better detected changes in microbial activity after PGPR applications, although FZB42 and Bioyield treatments only had greater activity than the control at 4 and 12 DAI. In general, enzymatic assays can be useful indicators of soil quality management and be related to soil health (Dick, 1997). Under field conditions, seasonal fluctuations may occur and those assays should have consistency in showing differences among treatments throughout the year. Detection of changes of microbial activity over time is the key point for an assay to be useful as indicator for PGPR application frequency in the field. Under greenhouse conditions, the FDA procedure was too variable to detect increased microbial activity, following applications of PGPR. Thus, if inconsistency occurs under greenhouse conditions, where environmental variables are under largely control, the FDA procedure cannot be recommended as a field assay for determining PGPR application frequency. Effects of PGPR applications on population size were also studied. Total culturable bacterial populations measured by plate counting, ranged from 1.1 x 10 8 to 5.2 x10 7 in FZB42 treatment to ~6 x 10 6 in Equity, PGA, and the nontreated control. Results suggested that the single strain PGPR (FZB42) colonized the root system and maintained higher population levels than PGPR products containing complex microbial communities (Equity and PGA). Equity and PGA are recommended as plant growth enhancers, while FZB42 also produces antibiotics. Antibiotic production might explain FZB42 colonization pattern and the increased total bacterial carrying capacity of the rhizosphere. FZB42 was an effective and persistent rhizosphere colonizer; once it was established, it increased total heat tolerant bacteria and thereby total population. FZB42 82 also exhibited a very distinguishable colony morphology, which made it more recognizable on plates inoculated with rhizosphere dilutions. Bioyield, a double-strain PGPR product, also increased total population within the same range as FZB42. Both FZB42 and Bioyield treatment markedly increased total heat tolerant bacteria population, with a few exceptions, throughout the experiment. Kokalis-Burelle et al., (2006) studied the effects of PGPR applied in the potting media, at transplanting, and during the growing season on indigenous rhizosphere microorganisms. When PGPR were applied in the potting media, they found increases in total heat-tolerant bacteria at all sampling times throughout the field trial. Additional PGPR applications during the growing season did not increase population size, although they did increase plant growth. Even if our experiments were conducted under greenhouse conditions, similar increases in total heat-tolerant bacteria, applied only at transplant, were recorded for Bioyield. Kokalis-Burelle et al., (2006) also obtained significant increases in total bacteria only at the end of the season, while we found those increases from the second sampling until the end of the experiment. PGPR products (single or double strains) did increase total heat-tolerant bacteria and were able to establish stable populations in the rhizosphere. Consequently, if root colonization is used as the criterion to decide when to reapply PGPR products, only applications in the potting media would be required. However, additional applications of PGPR may result in increases of plant growth and improved health, but not always in yield (Kokalis-Burelle et al., 2006). Increases in population were not related to changes in total microbial activity using the procedure suggested by Schnurer and Rosswall (1982). However, a recently 83 optimized FDA method (Green et al, 2006) was also used, and it performed better in the detection of changes in microbial activity after PGPR applications. Those changes were not always related to increases of either total bacteria or total-heat tolerant bacteria, meaning that microbial activity does not increase when population does. Overall, population size measured by direct plate counts could be a useful procedure to study root colonization and persistence of introduced microorganisms in the rhizosphere. Knowing that introduced microorganisms are surviving, and their patterns of growth will help to determine the application frequency of PGPR. In contrast, because of the lack of consistency, the FDA procedure is not useful for this purpose. We hypothesized that microbial inoculants can be used to induce suppressiveness to soil-borne pathogens by maintaining adequate microbial population and activity. PGPR have been reported for plant growth promotion in field and greenhouse conditions and also for pathogen suppression. During this study, positive effects on tomato growth were obtained and soil suppressiveness against M. incognita was induced. Additionally, increases in population size were detected by the direct plate counting for a single and double strain PGPR, although there was not a correlation between total microbial activity and population size. References 1. Barker, K.R., Koenning, S.R., 1998. Development of sustainable systems for nematode management. Annual Review of Phytopathology, 36: 165-205. 2. Chen, X-H., Vater, J., Piel, J., Franke, P.M, Scholz, R., Scheneider, K., Koumoutsi, A., Hitzeroth, Grammel, N., Strittmatter, A., Gottschalk, G., Sussmuth, R.D., Borriss, R., 2006. Structural and functional characterization of 84 three polyketide synthase gene clusters in Bacillus amyloliquefaciens FZB 42. Journal of Bacteriology, 188 (11): 4024-4036. 3. Cook, J.R., Baker, K.F., 1983. The Nature and Practice of Biological Control of Plant Pathogens. 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Soil ecosystem health and its role in plant disease suppression. In: Emerging Concepts in Plant Management, Research Singpost, Kerala, India. 15. Kokalis-Burelle, N., Kloepper, J.W., Reddy, M.S., 2006. Plant growth-promoting rhizobacteria as transplant amendments and their effects on indigenous rhizosphere microorganisms. Applied Soil Ecology. 31: 91-100. 16. Kloepper, J.W., Ryu C-M., Zhang, S., 2004. Induced systemic resistance and promotion of plant growth by Bacillus spp. Phytopathology, 94(11): 1259-1266. 17. Klump, R.S., and S.H. Thomas. 1987. Comparative resistance of selected Acala 1517 cotton cultivars to Meloidogyne incognita Race 3. Annual Applied. Nematology, 1: 113?115. 86 18. Lara, J., Acosta, N., Betancour, C., Vincente, N., Rodriguez, R., 1996. Biological control of Meloidogyne incognita in tomato in Puerto rico. Nematropica, 26: 143- 152. 19. Nico, A.I., Jimenez-Diaz, R.M., Castillo, P., 2004. Control of root-knot nematodes by composted agro-industrial wastes in potting mixtures. Crop Protection, 23: 581-587. 20. Pyrowolakis, A., Westphal, A., Sikora, R., Becker, J.O., 2002. Identification of root-knot nematode suppressive soils. Applied Soil Ecology, 19:51-56. 21. Schippers, B., Bakker, A.W.Bakker, P.A.H.M., 1987. Interactions of Deleterious and Beneficial Rhizosphere Microorganisms and the Effect of Cropping Practices. Annual review of Phytopathology, 25: 339-358. 22. Schnurer, J., T Rosswall., 1982. Fluorescein diacetate hydrolysis as a measure of total microbial activity in soil liter. Applied Environmental Microbiology 43, 1256-1261. 23. Sikora, R.A., Fernandez, E., 2005. Nematode parasites of vegetables. In: Luc, M., Sikora, R.A., Bridge, J. (Eds.) Plant-parasitic Nematodes in Subtropical and Tropical Agriculture. Second ed. CABI Publishing. Wallingford, UK, pp. 319- 392. 24. Weller, D.M., Raaijmakers, J.M., McSpadden, Gardener, B.B., Thomashow, L.S. 2002. Microbial populations responsible for specific soil suppressiveness to plant pathogens. Annual Review. Phytopatholology, 40: 309-348. 87 25. Weller, D.M., Thomashow, L.S., 2003. Take-all decline: a model for biocontrol of soil-borne pathogens. International Congress of Plant Pathology Abstracts and Proceedings. pp. 48-49. 26. Williamson V.M., Gleason, C.A., 2003. Plant-nematode interactions. Current Opinion in Plant Pathology, 6: 327-333. 27. Williamson V.M., Hussey, R.S., 1996. Nematode pathogenesis and resistance in plants. Plant Cell, 8: 1735-1745. 28. Wyss, U., Grundler, F.M.W., Munch, A., 1992. The parasitic behavior of second stage juveniles of Meloidogyne incognita in root of Arabidopsis thaliana. Nematologica 38:98-111. 88 Table 2.1 Effect of commercial PGPR products on fluorescein diacetate hydrolysis (FDA) using procedure suggested by Schnurer and Rosswall as a measure of total microbial activity in tomato rhizosphere soil FDA* Treatment Days after Inoculation 1 5 10 15 1. Equity? 1.61ab 2.23a 1.94 a 1.03 b 2. PGA? 1.50 b 1.61b 1.34 b 1.43 a 3. Ag Blend? 1.94 a 2.03a 1.44ab 1.33 a 4. FZB42? 1.80ab 1.49b 1.49ab 1.41 a 5. Control 1.74ab 2.07a 1.83ab 1.23ab LSD 0.05 0.425 0.383 0.593 0.217 Means followed by the same letter within a column are not significantly different. * FDA hydrolysis expressed as ?g fluorescein / g oven dry soil x h Table 2.2 Effect of microbial inoculants PGPR on total bacteria, total heat-tolerant bacteria, and total fluorescent pseudomonads Total population Total heat-tolerant bacteria Fluorescent pseudomonads (Log cfu/g) (Log cfu/g) (Log cfu/g) Treatment Days after inoculation 1 5 10 15 1 5 10 15 1 5 10 15 1. Equity? 7.34a 6.73b 6.57c 6.87b 3.05b 6.26b 6.12b 6.20b 5.29a 1.60b 2.35b 1.57b 2. PGA? 6.83b 6.63b 6.41c 6.65b 6.30a 6.31b 6.01b 6.28b 1.06b 3.01b 2.219b 1.60b 3. Ag Blend? 6.76b 6.63b 6.39c 6.71b 6.34a 6.24b 6.08b 6.28b 0.94b 1.67b 0.00c 2.40b 4. FZB42? 6.92b 8.01a 8.06a 7.75a 6.81a 7.27a 7.56a 7.22a 3.9ab 7.37a 6.36a 6.78a 5. Control 6.77b 6.70b 6.78b 6.87b 6.30a 6.33b 6.20b 6.25b 1.00b 2.83b 2.28b 3.69b LSD 0.05 0.41 0.18 0.19 0.30 1.67 0.16 0.21 0.12 3.17 2.60 2.14 2.24 Means followed by the same letter within a column are not significantly different. 89 90 Table 2.3 Effect of FZB42 on fluorescein diacetate hydrolysis (FDA) using procedure suggested by Green et al. (2006) as a measure of total microbial activity in tomato rhizosphere soil FDA* Treatment Days after Inoculation 1 4 8 12 16 1. FZB42? 0.16b 0.25a 0.26a 0.29a 0.27a 2. Control 0.27a 0.20b 0.25a 0.23b 0.26a LSD 0.05 0.058 0.055 0.053 0.04 0.096 Means followed by the same letter within a column are not significantly different. * FDA hydrolysis expressed as mg fluorescein / g dry soil x 3h 91 Table 2.4 Effect of Bioyield? on fluorescein diacetate hydrolysis (FDA) using procedure suggested by Green et al. (2006) as a measure of total microbial activity in tomato rhizosphere soil FDA* Treatment Days after Inoculation 1 4 8 12 16 1. Bioyield? 0.16b 0.35a 0.28a 0.30a 0.32a 2. Control 0.27a 0.21b 0.25a 0.23b 0.26a LSD 0.05 0.05 0.066 0.032 0.042 0.078 Means followed by the same letter within a column are not significantly different. *FDA hydrolysis expressed as mg fluorescein per g dry soil x 3h Table 2.5 Effect of FZB42 on total bacteria, total heat-tolerant bacteria, and fluorescent pseudomonads Total population Total heat tolerant Fluorescent pseudomonads Treatment (Log cfu/g) (Log cfu/g) (Log cfu/g) Days after inoculation 1 4 8 12 16 1 4 8 16 1 4 8 12 16 1. FZB42 7.87a 7.87a 7.92a 7.33a 7.76a 6.56a 6.02a 6.60a 5.94a 6.28a 6.89a 6.68a 6.90a 6.45a 2. Control 7.60b 7.94a 7.81a 7.34a 7.51b 5.00b 6.21a 6.00b 6.14a 6.25a 6.85a 6.29a 5.81b 6.23a LSD 0.05 0.21 0.12 0.10 0.20 0.19 0.11 0.47 0.18 0.50 0.19 0.36 0.82 0.33 0.30 Means followed by the same letter within a column are not significantly different. 92 Table 2.6 Effect of Bioyield on total bacteria, total heat-tolerant bacteria, and fluorescent pseudomonads Total population Total heat tolerant Fluorescent pseudomonads Treatment (Log cfu/g) (Log cfu/g) (Log cfu/g) Days after inoculation 1 4 8 12 16 1 4 8 16 1 4 8 12 16 1.Bioyield? 7.68a 7.95a 8.01a 7.83a 7.81a 6.67a 6.88a 6.73a 6.83a 6.04a 4.87a 6.21a 6.33a 6.00a 2. Control 7.60b 7.94a 7.81b 7.34b 7.51b 5.00b 6.21a 6.00b 6.14a 6.25a 6.85a 6.29a 5.81a 6.23a LSD 0.05 0.18 0.19 0.17 0.21 0.19 0.07 0.45 0.21 0.38 0.27 2.22 0.74 0.54 0.32 Means followed by the same letter within a column are not significantly different. 93 94 Table 3.1 Effect of microbial inoculants (PGPR) on root architecture related variables one day after inoculation Treatment Length (cm) Surface area (cm 2 ) Volume (cm 3 ) Root diameter (mm) Number of tips 1. Equity? 101.8a 66.4a 3.5ab 2.1a 283.2a 2. PGA? 106.6a 73.2a 4.1a 2.2a 213.2ab 3. Ag Blend? 100.5a 62.9a 3.1bc 2.0a 206.0ab 4. FZB42? 89.6a 50.5b 2.3c 1.8a 198.8a 5. Control 106.6a 67.0a 3.5ab 2.1a 207.8ab LSD 0.05 26.07 31.1 0.89 0.42 80.07 Means followed by the same letter within a column are not significantly different. 95 Table 3.2 Effect of microbial inoculants (PGPR) on plant growth measurements one day after inoculation Treatment Shoot Fresh Weight (g) Shoot Dry weight (g) Growth Index (cm 2 ) Root Fresh Weight (g) Root dry Weight (g) Height (cm) 1. Equity? 0.87c 0.16b 50.00b 0.82ab 0.04ab 5.20a 2. PGA? 1.70a 0.24ab 109.20a 0.89a 0.05a 5.40a 3. Ag Blend? 1.82a 0.33a 107.40a 0.60c 0.04b 5.20a 4. FZB42? 1.35b 0.17b 103.40a 0.76b 0.04ab 6.20a 5. Control 1.82a 0.30a 120.20a 0.78ab 0.03b 5.60a LSD 0.05 0.253 0.09 28.65 0.125 0.0128 1.135 Means followed by the same letter within a column are not significantly different. 96 Table 3.3 Effect of microbial inoculants (PGPR) on root architecture related variables five days after inoculation Treatment Length (cm) Surface area (cm 2 ) Volume (cm 3 ) Root diameter (mm) Number of tips 1. Equity? 163.2a 129.7a 8.3a 2.5a 358.2a 2. PGA? 193.4a 156.2a 10.2a 2.6a 407.8a 3. Ag Blend? 148.2a 117.6a 9.1a 2.8a 320.2a 4. FZB42? 194.9a 153.6a 9.7a 2.5a 411.2a 5. Control 191.6a 147.0a 9.2a 2.4a 353.4a LSD 0.05 49.82 42.7 3.8 0.48 129.8 Means followed by the same letter within a column are not significantly different. 97 Table 3.4 Effect of microbial inoculants (PGPR) on plant growth measurements five days after inoculation Treatment Shoot Fresh Weight (g) Shoot Dry Weight (g) Growth Index (cm 2 ) Root Fresh Weight (g) Root dry Weight (g) Height (cm) 1. Equity? 2.76b 0.43b 203.20ab 2.06a 0.09a 7.10b 2. PGA? 2.72b 0.44b 201.00ab 1.95a 0.01a 6.80a 3. Ag Blend? 3.62a 0.48ab 251.00a 1.92a 0.08a 7.30ab 4. FZB42? 2.05c 0.31c 164.80b 1.80a 0.10a 7.20b 5. Control 3.52a 0.52a 234.00a 2.31a 0.10a 8.10a LSD 0.05 0.62 0.076 52.1 0.53 0.023 0.85 Means followed by the same letter within a column are not significantly different. 98 Table 3.5 Effect of microbial inoculants (PGPR) on root architecture related variables ten days after inoculation Treatment Length (cm) Surface area (cm 2 ) Volume (cm 3 ) Root diameter (mm) Number of tips 1. Equity? 340.4a 263.0a 17.5a 2.5a 798.4a 2. PGA? 336.8a 249.2a 14.8a 2.4a 750.0ab 3. Ag Blend? 267.8ab 286.5a 25.3a 3.5a 462.0c 4. FZB42? 230.9b 233.9a 22.7a 3.6a 610.0abc 5. Control 267.7ab 274.1a 23.0a 3.3a 547.0bc LSD 0.05 85.41 78.13 12.78 1.32 222.9 Means followed by the same letter within a column are not significantly different. 99 Table 3.6 Effect of microbial inoculants (PGPR) on plant growth measurements ten days after inoculation Treatment Shoot Fresh Weight (g) Shoot Dry Weight (g) Growth Index (cm2) Root Fresh Weight (g) Root dry Weight (g) Height (cm) 1. Equity? 5.33b 0.87b 275.40b 4.81ab 0.25a 9.20a 2. PGA? 4.98b 0.81b 255.60b 4.54ab 0.20ab 8.50a 3. Ag Blend? 8.35a 1.14a 353.60a 5.32a 0.25a 9.40a 4. FZB42? 4.42b 0.58c 259.80b 3.82b 0.14b 9.00a 5. Control 4.93b 0.78bc 229.80b 5.29a 0.22a 8.60a LSD 0.05 1.03 0.2 65.38 1.21 0.067 1.29 Means followed by the same letter within a column are not significantly different. 100 Table 3.7 Effect of microbial inoculants (PGPR) on root architecture related variables fifteen days after inoculation Treatment Length (cm) Surface area (cm 2 ) Volume (cm 3 ) Root diameter (mm) Number of tips 1. Equity? 364.8ab 396.7ab 30.5b 3.3ab 729.0a 2. PGA? 372.7ab 355.8ab 27.4b 30.3b 779.8a 3. Ag Blend? 317.5b 407.7a 46.3a 4.1a 748.0a 4. FZB42? 329.2b 318.5b 24.5a 3.1b 778.0a 5. Control 416.4a 390.4ab 30.8a 3.1b 814.6a LSD 0.05 66.2 79.27 14.75 0.86 172.25 Means followed by the same letter within a column are not significantly different. 101 Table 3.8 Effect of microbial inoculants (PGPR) on plant growth measurements fifteen days after inoculation Treatment Shoot Fresh Weight (g) Shoot Dry Weight (g) Growth Index (cm 2 ) Root Fresh Weight (g) Root dry Weight (g) Height (cm) 1. Equity? 8.10ab 1.32b 264.00ab 7.32ab 0.40ab 10.60ab 2. PGA? 6.87b 1.46b 271.00ab 7.58ab 0.36ab 9.60b 3. Ag Blend? 10.60a 2.06a 353.60a 9.40a 0.46a 10.80ab 4. FZB42? 8.40ab 1.24b 347.00ab 5.89b 0.35b 11.60a 5. Control 6.48b 1.22b 243.60b 6.20b 0.32b 10.40ab LSD 0.05 3.03 0.59 104.68 2.38 0.1 1.8 Means followed by the same letter with a column are not significantly different. 102 Table 4.1 Effect of three PGPR microbial inoculants on nematodes and plant growth* Treatment Shoot Fresh Weight (g) Shoot Dry Weight (g) Root Dry Weight (g) Gall Rating Egg/g Juveniles 1. Equity? 25.5a 3.5a 23.4ab 3.5b 5798a 1226bc 2. Bioyield? 27.0a 3.5a 30.7a 1.7c 1940b 815c 3. Ag Blend? 25.2a 3.5a 26.9a 3.3b 4368a 1708ab 4. Control 24.1a 3.3a 22.0b 4.5a 4244a 1896a LSD 0.05 3.64 0.51 7.64 0.68 2412 436.5 Different letters within a column indicate statistically significant difference (P=0.05) among treatments. *This table corresponds to the first and second time the experiment was conducted. 103 Table 4.2 Effect of three PGPR microbial inoculants on nematodes and plant growth* Treatment Shoot Fresh Weight (g) Shoot Dry Weight (g) Root Dry Weight (g) Gall Rating Egg/g Juveniles 1. Equity? 18.1a 2.6a 22.4a 1.1a 481.5ab 405.6a 2. Bioyield? 10.0b 2.6a 9.1c 0.0b 195.9b 97.0b 3. FZB42? 18.8a 2.8a 19.0ab 1.1a 361.3ab 39.0b 4. Control 16.4a 1.0b 16.3b 1.1a 807.7a 405.6a LSD 0.05 4 0.483 4.87 0.443 558.42 264.3 Different letters within a column indicate statistically significant difference (P=0.05) among treatments. *This table corresponds to the third time the experiment was conducted. 104 Table 4.3 Effect of three PGPR microbial inoculants on nematodes and plant growth Treatment Shoot Fresh Weight (g) Shoot Dry Weight (g) Root Dry Weight (g) Gall Rating Egg/g Juveniles 1. Equity? 24.4a 3.1a 13.5b 3.4b 6032ab 536.0b 2. Bioyield? 22.5a 3.2a 22.8a 2.9b 4679b 902.9ab 3. FZB42? 21.7a 3.3a 13.6b 2.9b 4753b 671.3b 4. Control 21.9a 3.3a 13.9b 4.0a 11045a 1376.1a LSD 0.05 11.815 0.79 4.48 0.6 5714 564.5 Different letters within a column indicate statistically significant difference (P=0.05) among treatments. *This table corresponds to the fourth time the experiment was conducted. 105 Table 5.1 Effect of three PGPR microbial inoculants on total bacteria, total heat- tolerant bacteria, and FDA hydrolysis in tomato rhizosphere soil Treatment Total bacteria Total heat- tolerant bacteria FDA (log cfu/g) (log cfu/g) 1. Equity? 7.24b 6.33bc 1.69a 2. Bioyield? 7.68a 6.90a 2.42a 3. Ag Blend? 7.10b 6.27bc 1.51a 4. Control 7.16b 6.04c 1.88a LSD 0.05 0.371 0.41 0.967 Different letters within a column indicate statistically significant difference (P=0.05) among treatments. *This table corresponds to the first time the experiment was conducted. 106 Table 5.2 Effect of three PGPR microbial inoculants on total bacteria and total heat-tolerant bacteria in tomato rhizosphere soil* Treatment Total bacteria Total heat-tolerant bacteria (log cfu/g) (log cfu/g) 1. Equity? 7.00b 6.29b 2. Bioyield? 7.34a 6.88a 3. FZB42? 7.42a 6.43a 4. Control 7.06 6.08b LSD 0.05 0.239 0.26 Different letters within a column indicate statistically significant difference (P=0.05) among treatments. *This table corresponds to the third and fourth time the experiment was conducted. 107 SUMMARY In recent years, use of microbial inoculants for plant growth promotion has increased. Examples of microbial inoculants are Bioyield?, which contains a mixture of two strains (Bacillus subtilis and B. amyloliquefaciens), FZB42, a single strain of B. amyloliquefaciens and Soil Builder?, Ag Blend?, and Equity?, which contain complex mixtures of over 10 strains of Bacillus spp. Optimizing application frequency of PGPR is critical to achieving the maximum benefit from this technology. Since the rhizosphere is considered the most intense ecological habitat in soil, it is of interest to study the effects that PGPR may have on total microbial activity and bacterial population in the zone where rhizobacteria exerted a direct influence on plants. The objectives of this study were to evaluate a set of sensitive methods to detect increases in microbial activity following additions of microbial inoculants and to determine the relationship among soil microbial activity, microbial population and disease suppressiveness after the addition of soil inoculants PGPR. Commercial formulations of PGPR, containing bacilli strains (Equity?, Soil Builder?, Ag Blend?, PGA?, Bioyield? and FZB42?) were used on tomato and strawberry in greenhouse and field experiments. Physiological activity of microbes was measured by assessing dehydrogenase activity, arylamidase activity, and fluorescein diacetate hydrolysis (FDA). Culturable microbial populations were determined by most 108 probable number (MPN) and direct plate counting. In strawberry field trials, hydrolysis of FDA was significantly different among treatments at one of four sampling times. Procedures to estimate population size (MPN) did not detect any change in microbial population; however, the use of PGPR inoculants promoted growth and increased strawberry yield. In greenhouse experiments on tomato, FDA was effective in measuring changes in microbial activity in the rhizosphere following inoculants application, while arylamidase and dehydrogenase procedures were not sensitive in detecting those changes. Despite detecting changes in microbial activity, no changes in microbial populations, estimated by MPN, were observed. Thus, little or no correlation was detected between microbial enzymatic activity and bacterial population with the procedures used. Populations of total of culturable and heat-tolerant bacteria were also measured by plate counting at 1, 5, 10 and 15 days after PGPR inoculation. FZB42 and Bioyield treatments generally resulted in significantly greater total populations than the control. FZB42 exhibited a very distinguishable colony morphology, which made it more recognizable on plates inoculated with rhizosphere dilutions. Induction of soil suppressiveness by PGPR and the relation to microbial activity and population size were also studied. The plant parasitic nematode Meloidogyne incognita and tomato were used as a model. Three-week-old tomato seedlings were first inoculated with PGPR products and then challenged with nematode eggs. Harvest was done 45 days after nematode inoculation, and rhizosphere soil samples were taken for microbial activity and population size determinations. 109 Results showed significant reductions in numbers of nematode eggs per gram of root, numbers of juveniles per ml and numbers of galls in FZB42 and Bioyield treatments. Additionally, increases in population size were detected for those treatments by the direct plate counting, although there was not a correlation between microbial activity and population size. Overall, population size measured by direct plate counts could be a useful procedure to study root colonization and persistence of introduced microorganisms in the rhizosphere. Knowing that introduced microorganisms are surviving, and which their patterns of growth are will help to determine when and how these PGPR products should be applied. However, because of the lack of consistency, FDA procedure should not be used to decide the frequency of application of PGPR products.