Effects of Tillage, Rotation, and Organic Inputs on Soil Ecological Properties in Vegetable Crop Production Systems

Final Report for GS04-035

Project Type: Graduate Student
Funds awarded in 2004: $9,998.00
Projected End Date: 12/31/2007
Grant Recipient: NCSU
Region: Southern
State: North Carolina
Graduate Student:
Major Professor:
Greg Hoyt
Dept. of Soil Science, NCSU
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Project Information

Summary:

Biological properties generally responded to all treatment combinations, but tillage provided the strongest treatment effect in most cases. Compared to strip-tillage, moldboard tillage consistently yielded significantly lower values for the following biological measurements: total C and N, above-ground biomass, microbial biomass, enzyme activity, soil respiration, N mineralization, some nematode trophic groups, and earthworms. Compared with organic inputs, synthetic inputs consistently induced significantly lower values for the following biological measurements: microbial biomass, enzyme activity, some nematode trophic groups, and soil respiration. An examination of relationships between biological and physical parameters using redundancy analysis revealed that microporosity was the physical property that was most strongly correlated with most biological parameters. Soil organisms responded to treatments in the following order: tillage > input > rotation.

Tables, figures or graphs mentioned in this report are on file in the Southern SARE office.
Contact Sue Blum at 770-229-3350 or
sueblum@southernsare.org for a hard copy.

Introduction

The biological composition of soils is described in terms of biological diversity, the food web, and the richness, abundance, and distribution of species. The soil biological community includes all organisms that live in the soil for at least part of their life cycle and includes species of bacteria, fungi, protozoa, algae, nematodes, arthropods, and worms. Biodiversity of soils is commonly thought of in the context of ecosystems services, or functional roles provided by different subsets of the soil biological community. A selection of the ecosystem services provided by the soil biota includes decomposition of organic matter, and, conversely C retention, nutrient cycling, bioturbation, and suppression of soil-borne diseases and pests (Brussaard et al., 1997). Biological composition is highly variable in soils and dependent upon a number of factors including soil type, moisture content, aeration, organic matter, nutrient availability, temperature, and soil structure.
Ecology is defined as the study of relationships of organisms with each other and their physical environment (adapted from Smiles, 1988). There is no question that organisms are strongly influenced by and also react to their physical habitat. Soil organisms, however, are frequently overlooked despite their critical roles in the functioning of an ecosystem due to their small size, hidden behavior, and uncertainty regarding their contribution to soil processes. Organisms’ responses to changes in their physical and chemical environment are only well enough understood to make broad generalizations.
Soil structure can be evaluated in different ways, but perhaps the most meaningful measurement of structure is an evaluation of the size, configuration, and distribution of soil pores (Danielson and Sutherland, 1986). Soil structure responds to and concurrently exerts a strong influence on the organisms that reside in the soil. The distribution of different pore-size classes and the size of pore openings (i.e. “pore necks”) is often more important for characterizing the soil as a biological habitat than is an evaluation of the soil particles. Precise quantification of soil porosity characteristics such as neck-size and tortuousity is essentially impossible at the present time, due to the complicated nature and opaque fabric of the soil matrix (Danielson and Sutherland, 1986). It is possible, however, to determine pore space with relatively high precision, and by making certain assumptions, the pore-size distribution can be made with at least useful accuracy for both laboratory and field purposes (Danielson and Sutherland, 1986).
Many environmental factors (e.g., climate, soil texture, soil parent material, plant community, management, or season) are critical in determining the composition of soil microbial communities (Bossio et al., 1998). Plants are certainly an important determinant of the soil food web structure. For a given soil, the types and diversity of vegetation are significant determinants of biota diversity and distribution. Plants and their fungal symbionts, cyanobacteria, and algae, serve as the primary producers for all terrestrial ecosystems, and as such control the amounts of C that enter the soil system (Loreau et al., 2001; Wardle, 2002). The variety and complexity of C inputs is influenced by the individual plant species and the assemblage of plant species present in an area (Giller et al., 1997). Since plants respond to their environment with different physiological growth responses, including production of root exudates, growth hormones, and characteristic C:N ratios, the chemical, physical, and biological components of the environment influence the C inputs released to the soil by plants. One may consider the chemical quality of the plant community to be the fiber of which the soil food web is constructed. The primary decomposers of plant litter and the plant pathogen and herbivore species are the foundation upon which the soil food web is spun.
Although plants and other autotrophs are the primary drivers of the soil food web, the heterotrophic organisms which feed directly on the primary producers ultimately dictate the availability of nutrients required for plant growth as well as for the remaining soil microflora and fauna (Sparling, 1985; Jenkinson, 1988). Thus, the plant and decomposer communities are in an obligately mutualistic relationship in which each of the two components relies on the other for their long-term sustenance (Wardle, 2002). The ecology of decomposers is an especially poorly understood area of soil ecology (Neher, 1999) and research results are frequently contradictory among studies.
When considering the primary decomposers of soil organic matter, it is beneficial to remember that microorganisms are responsible for about 90% of the C mineralization resulting from the decomposition of organic compounds (Swift and Anderson, 1993). Other organisms which are also considered primary or secondary decomposers include mites, millipedes, earthworms and termites, and these organisms play an integral role in shredding organic residues and dispersing microbial populations, thereby allowing microorganisms to function more efficiently (Brussaard et al., 1997). The activities of the decomposer species are responsible for most of the biochemical transformations of organic matter, resulting in nutrient mineralization and organic matter humification (Brussaard and Juma, 1996).
Soil microbial biomass is also considered to be a part of the labile soil organic matter, and as such, is an important component of nutrient cycling (Smith, 1979; Paul, 1984). Steenwerth et al. (2002) found in their study of the soil microbial community composition of nine land use types that differences in the community structure of microorganisms were most highly correlated with soil microbial biomass and pH and soil management factors such as fertilizer, herbicide, and irrigation. Bossio et al. (1998) measured phospholipid fatty acid profiles in soils from organic, low-input, and conventional farming systems and ranked the environmental variables governing the composition of microbial communities in their study in the following order: soil type>time>specific farming operation (e.g., cover crop incorporation or side dressing with mineral fertilizer)>management system>spatial variation in the field.
Nematode biodiversity is resistant to reduction of species richness resulting from environmental stressors such as low moisture, chemical biocides, tillage practices, and cropping rotations (Niles and Freckman, 1998; Freckman and Virginia, 1991). Nematode community structure, however, is a sensitive indicator of environmental stressors in terrestrial ecosystems when evaluated at the higher-order (less specific) resolution of nematode feeding groups (Elliott, 1994; Moore and de Ruiter, 1993; Parmalee et al., 1993; Platt et al., 1984; Wardle et al., 1995; Yeates et al., 1991).
Nematodes are a major component of the soil food web in their role as grazers of bacteria and fungi, and thus influence the rate of organic matter decomposition and nutrient turnover. An additional physiological characteristic of nematodes is their metabolic efficiency. Wasilewska et al. (1981) found that bacterial-feeding and fungal-feeding nematodes defecated 80% of the material they consumed in a study of decomposition in a Polish rye field (Secale cerale L.). Depending on environmental conditions, nematodes can directly and indirectly influence organic matter decomposition rates and have been found to strongly enhance the rate of C and nutrient mineralization in some studies (De Ruiter and Moore, 1993; Hunt et al. 1987). Rationale for the use of nematodes in bioassessment efforts is described by Niles and Freckman (1998) as follows: i) environmental chemical stressors are integrated at the organ level in nematodes rather than the cellular level as in bacteria and protozoa; ii) short generation times allow nematodes to respond quickly to many environmental stressors; iii) nematodes are able to survive in polluted soils; iv) because nematodes are limited in their motility to only a few centimeters in soil, they allow the investigation of relatively small spatial areas for environmental growth conditions; v) nematodes influence the productivity and decomposition functions of soil; vi) nematode feeding groups (higher resolutions of biodiversity) inhabit virtually all soils on earth and inflict changes in the soil habitat; and vii) methods for nematode sampling, extraction, and identification are generally straightforward and easy to perform.
Another influential subset of the soil biota is that which alters the physical properties of soils. These organisms are commonly referred to as the soil bioturbators or bioengineers. Earthworms, ants, and termites are the most commonly cited examples of this functional group. Earthworms make the broadest contributions to soil physical property change, especially in agricultural soils. The effects of earthworms on soil structure, formation of heterogenous pores, and increased aggregate stability are hallmarks of soil-biota interactions over long periods of time (Coleman and Crossley, 2003). Both decomposers and bioturbators indirectly influence plant growth (Brussaard, 1997).
Earthworms are widely known to perform many beneficial functions in agricultural systems including shredding residues, enhancing microbial degradation, improving soil fertility, and improving soil physical properties such as water infiltration and aggregate stability. Agricultural management practices strongly impact earthworm populations. The most significantly negative effect on earthworm populations related to agricultural practices is tillage. As the frequency and intensity of tillage operations increase, so does the physical destruction of earthworm burrows, cocoons, and the earthworm bodies themselves (USDA, 2001). Tillage also indirectly affects earthworm abundance by regulating the rate of decomposition and the availability of surface residues. Availability of organic matter is the most important factor related to the soil environment which influences earthworm abundance because organic matter serves as a food source, insulates earthworms from adverse weather, provides shelter from birds and other surface predators, and protects earthworm burrows (USDA, 2001). Studies investigating the effect of different fertilizer sources show that nearly all fertilizers benefit earthworms. Addition of organic sources of nutrients (e.g. manure addition, compost, organic fertilizers) can double or triple earthworm numbers in a single year (Edwards and Bohlen, 1996). The use of inorganic fertilizers has also been found to have a generally positive effect on earthworm numbers, which has been attributed to the indirect effect of increased crop biomass and resulting increases in organic residues (Edwards and Bohlen, 1996; Edwards et al., 1995). In regard to pesticides, most herbicides used today have been found to be harmless to earthworms (USDA, 2001). Insecticides, however, have varying degrees of toxicity for earthworms, depending on the class of chemical used. Insecticides containing carbamates and certain organophosphates are highly toxic to earthworms, as are broad-spectrum fumigants such as fungicides and nematicides (Ernst, 1995; Edwards and Bohlen, 1996).
Agriculture is one of the most obvious examples of how natural soil ecosystems have been drastically altered by humans. The very idea of selectively planting and managing the growth of certain plants for human consumption, and, later, for forage and fiber production, was a hypothesis that was tested by humans in the early ages of our existence and found to be inordinately useful – eventually leading to the agrarian societies our lives today are founded upon. But hundreds of years of artificial manipulation of agricultural soils have dramatically changed the biological and functional capacity of these soils. Giller et al. (1997) expounded on this, pointing out that as agricultural intensification occurs, the biological regulation of soil functions is progressively replaced by regulation through chemical and mechanical means. Therefore, agricultural settings are one of the most logical places to examine the manner in which soil-dwelling organisms respond to calculated changes in their physical and chemical soil habitat.
Soil management factors related to agricultural practices have a wide range of effects on the composition of soil organisms. These practices have both positive and negative consequences with regard to total numbers and diversity of soil organisms. Organisms are affected by agricultural practices both directly and indirectly (Stubbs et al., 2004). Direct effects include bodily damage, habitat destruction, poisoning due to toxic doses of pesticides from target and non-target effects of agricultural chemicals, increased availability of nutrients from fertilizer applications, moisture differences due to evapotranspiration, drainage, and irrigation, and pH differences due to soil amendments. Indirect effects are probably more significant and extensive than direct effects to soil organisms and include soil C reduction due to tillage practices, reduction in complexity and diversity of C inputs due to the reduced diversity of vegetation in the agricultural field, compaction, disturbance of trophic interactions resulting from selective pressure exerted on microbes, and residual toxicity and break-down products of biocide applications (Coleman et al., 1993; Pižl, 1993; Steenwerth et al., 2002; ).
Agricultural management practices such as tillage, residue incorporation, irrigation, and rotation sequence can affect soil microbial biomass (Anderson and Gray, 1990; Sparling et al., 1994; Franzluebbers et al., 1995) as well as soil microbial community composition (Bossio et al., 1998; Lundquist et al., 1999; Calderón et al., 2000). Agricultural practices are recognized to have a strong effect on the functional activity of soil microorganisms (Doran et al., 1987; Coleman et al., 1993). The exact nature of the impacts of agricultural practices such as conventional tillage, monocropping, and chemical fertilizers and pesticides (including fumigation) on the soil microbial community is not well understood and results obtained are often variable in different environments (Capri et al., 1993; Wardle et al., 1999). This reflects the increased complexity of soil ecosystems as compared to air and water systems, for which relatively much more is understood in terms of environmental toxicology (Edwards, 2002). Since soils are one of the two main ultimate sinks for chemical pollutants (the other being water), there is a recognized need to increase our understanding of the effects of conventional agricultural practices on soil microbial communities, as well as how those effects are augmented in subsequent biological trophic interactions, soil fertility, and soil physical properties.
This study investigates three key functional biological groups (microorganisms, nematodes, and earthworms), in a field-scale research experiment originally designed to examine long-term crop yields and pest interactions resulting from three different agricultural management decisions. We selected representative organisms of the microflora, microfauna, and macrofauna classification systems based on relative importance for trophic interactions, nutrient cycling processes, and ability to induce soil physical property change. Body size is commonly used as a functional classification system because it is directly related to metabolic rate, generation time, population density, and food size (Mikola et al., 2002). We construct the composition of the microbial community using taxonomic and functional enumeration techniques. We characterize the nematode and earthworm communities as key representatives of the soil micro- and macrofauna communities that are responsible for significant roles in nutrient cycling and soil physical properties, respectively. Finally, we attempt to establish the linkages between the relative combined presence of these members of the soil community and agricultural practices common to conventional and alternative agricultural systems.
Ecology is defined as the study of relationships of organisms with each other and their physical environment (adapted from Smiles, 1988). There is no question that organisms are strongly influenced by and also react to their physical habitat. As a scientific discipline, soil ecology has only been recognized for two decades and is still frequently overlooked by ecologists (Neher, 1999). This is partially a result of the complexity of soil systems, the broad diversity of soil organisms, the difficulty associated with extracting and identifying soil organisms, and a lack of recognition of the role of soil biota in determining the physical and chemical properties and production potential of soil (Hawksworth and Mound, 1991; Neher, 1999; Loreau et al., 2001). Although soils contain by far the greatest diversity of organisms present in most terrestrial ecosystems, Wardle (2002) estimates that less than 3% of papers published in major ecological journals address studies of belowground organisms. Thus, Wardle and Giller (1996) conclude that the current body of ecological literature and the seminal concepts of terrestrial ecology are based upon only a minority of the Earth’s biota.
The evolution of soil biological communities depends on physical, chemical, and biological soil properties as well as the organisms themselves. The way that these factors come together and affect one another is of great interests to soil ecologists. A conscientious analysis of this topic requires multivariate analytical procedures to quantitatively evaluate relationships and interactions between soil physical properties, management treatments, and soil organisms.
Phospholipid fatty acid (PLFA) analysis is a biochemical method that provides information regarding the soil microbial community composition (Vestal and White, 1989). It may be possible to examine specific PLFAs within a soil to represent indicators of taxonomic or functional groups (Parkes, 1987). Lipids account for 2-20% of the mass of most bacteria, 10-20% of the mass for most fungi, and 2-15% for algae (Jones, 1969; Rattray et al, 1975). Bacteria contain a large diversity of fatty acids including normal, straight-chain, monounsaturated, branched-chain, 2- and 3-hydroxy and cyclopropane configurations (Lechevalier and Lechevalier, 1988). Bacterial polyunsaturated fatty acids are rare (Lechevalier and Lechevalier, 1988). Algae are the microorganisms having the greatest diversity of polyunsaturated fatty acids (Shaw, 1966). Protozoa produce a more limited range of fatty acid compounds and fungi are the most restricted group, most commonly only having 18:2 and 18:3 polyunsaturated fatty acids (Lechevalier and Lechevalier, 1988). Phospholipids, in particular, are apparently always associated with membranes, and certain phospholipids are more common in some groups of microorganisms than in others (Lechevalier and Lechevalier, 1988), which is the principle that makes it useful for characterizing the soil microbial community.
While PLFAs can be useful for examining microbial diversity from a taxonomic perspective, it reveals nothing about the functional diversity of the soil microbial community. Evaluation of enzyme activities corresponding to microbial functions is one way to examine the functional diversity of soils resulting from differing agricultural management systems (Dick et al., 1996; Aon et al., 2001). Fumigation, fertilizer differences, and tillage management are factors in agricultural management that can impact the microorganisms which release many exoenzymes responsible for key soil functions. A reduction in the taxonomic diversity of soils may not impact the functional diversity of soils if there is a high degree of functional redundancy in the soil microbial community (Fonseca and Ganade, 2001; Giller et al., 1997). This occurs because a single soil function is often conducted by a large number of species, so if one of those species is eliminated it has little effect on the function itself because other organisms will fill the functional role (Walker, 1992; Lawton and Brown, 1993). Despite a great deal of time, energy, and money spent researching the consequences of reduced microbial activity resulting from increased agricultural intensity, there is no substantive in situ evidence indicating that microbial diversity is reduced from a taxonomic or functional perspective as a result of intensive agricultural activity (Giller et al., 1997). It is tempting, of course, to predict that soil biodiversity would decrease given that the above-ground diversity of plants and animals is reduced in agriculture fields and, also, that soil disturbance (tillage) often increases as agriculture endeavors become increasingly intensive (Giller et al, 1997). However, chemical pest control substances and tillage both have unpredictable effects on the species diversity of various groups of fauna (Wardle, 1995).
Studies differ in the results achieved by investigating the effect of reduced tillage on pore-size distribution. Some studies have compared pore-size distribution between no-till and conventional tillage and found that macroporosity increased in no-till treatments compared to conventionally plowed treatments (Eliott and Coleman, 1988). Other studies have shown that increases in soil organic matter have variable effects on macro- and microporosity, depending on soil texture. Scheffer and Schachtschabel (1989, translated to English by Kirchmann and Gerzabek, 1999) reported that in fine-textured soils, there was a reduction in macroporosity as a result of reductions in organic matter, but in coarse-textured soils reductions in micropores were observed. Messing et al. (1997) observed that macropores (>75 μm) were formed as a result of higher soil C levels in sandy soils. Azooz et al. (1996) found that the effect of no-till in a silt loam and a sandy loam was to reduce the proportion of large pores and increase the proportion of small pores relative to conventional moldboard plowing. Azooz et al. (1996) concluded that the effect of tillage management on the volume of large pores is specific to the soil and cropping system and further research is required. They suggested evaluating pore size changes due to cropping management under different environmental conditions and soil textures.
This study investigates the way that soil organisms exist and interact with their physical environment. We were fundamentally interested in the soil physical and biological properties resulting from different long-term agricultural management decisions and the relationship between these physical and biological entities. By using phospholipid analysis along with traditional biological activity measurements such as C and N mineralization, we attempted to characterize the soil microbiological community. We extracted the phospholipids present in soil samples from agricultural treatments and compared the phospholipid signatures as indicators of differing microbial community structures resulting from agricultural management decisions. We also evaluated the nematode populations of these agricultural management treatments and related all of these measurements to soil physical properties resulting from the implemented management decisions.
Conservation tillage is broadly defined as any tillage practice that maintains residue cover on at least 30% of the soil surface area (Conservation Tillage Information Center, 1988). Strip-tillage is a conservation tillage practice that isolates soil tillage to a narrow band, generally 15-45 cm in width, using a specialized tractor implement. Strip-tillage incorporates the environmental and crop growth benefits of no-till with the improved root environment associated with tillage practices. Strip-tillage also provides unique soil physical properties compared to conventional or no-tillage practices because the soil matrix undergoes an intermediate level of disturbance relative to these two extreme practices (Hill, 1990; Vyn and Raimbault, 1993).
Positive effects of strip-tillage related to plant growth include factors associated with improved seed bed and rooting environment, decreased surface bulk density, increased moisture content between the rows, soil resistance to negative effects of heavy equipment traffic, and increased yields relative to conventional tillage (Raper et al., 1994). Potential crop benefits related to soil fertility have also been reported and include a more readily mineralizable pool of N and more plant-available P compared to conventional tillage (Kingery et al., 1996). Al-Kaisi and Hanna (2002) report that strip-tillage can improve the seedbed environment in poorly-drained soils due to increases in soil moisture evaporation and increased soil temperature in-row compared to no-till practices.
Other agricultural management practices that have become widely adopted for a variety of reasons are organic production systems and crop rotations. The market for organic produce has seen tremendous growth in the past 15 years. Since 1990, annual growth of organic products has equaled or exceeded 20 percent in retail sales nationally and U.S. certified organic cropland doubled between 1992 and 1997 to 1.3 million acres (Dimitri and Greene, 2002). Interest in rotational cropping has also grown in recent years. In 1997, 82% of the 196 million acres of total U.S. cropland was in some kind of a rotation system (Padgitt et al., 2000). Interest in crop rotations is particularly high for vegetable and alternative crops in the southeast United States as tobacco has become a less economically reliable commodity. Alternative crop management systems offer many advantages to growers and often command a higher price than conventionally-grown crops, especially in the vegetable market. Although it is doubtful that these alternative systems will altogether replace conventional methods of vegetable production in the foreseeable future, there is certainly the potential for these systems to become an accepted and integrated part of the conventional vegetable production system in the southeast United States.
The soil biological community is responsible for many critical crop growth processes including nutrient cycling, soil structure change, and organic matter accumulation/degradation. Therefore, it is important to monitor and assess biological differences between conventional cropping systems and these less-intensive systems (i.e. strip-tillage, organic inputs, and crop rotations). The biological consequences of these alternative management systems have not been studied on a long-term scale in vegetable cropping systems (Hummel et al., 2002).
The extent to which strip-tillage, organic inputs, and crop rotation affect other areas of the field (e.g. the inter-row area between strips) is also unknown. It has been demonstrated that soil biological community composition and biomass varies widely from the rhizosphere to the bulk soil, resulting in significant differences in microbial activity and processes related to soil function, which include soil C sequestration, N dynamics, plant nutrient availability, and litter decomposition (Söderberg and Bååth, 2004). More specific differences in the soil biological activity and composition between a large area of even plant coverage (i.e. inter-rows) and a relatively smaller area of select vegetation (i.e. in-row areas), as seen in strip-tillage systems, is not known. Measurements made for agroecological investigations in conservation tillage studies have been mainly limited to the crop row or crop root zone area. However, since the virtues researchers often extol regarding the advantages of conservation tillage revolve around the fact that reduced tillage systems leave cover crops or previous crop residues on the soil surface, it is of some importance to characterize the ecosystem of the entire field, including the inter-row areas which are frequently cited as the source of environmental and crop-related benefits (e.g. increased C retention, moisture holding capacity, nutrient cycling). It is our impression that previous researchers have treated the inter-row areas of reduced tillage fields as similar to a non-cropped ecosystem, such as a turf grass or pasture system. In the case of strip-tillage, most studies have considered this system as a sort of hybrid between a conventional tillage system (within the strip) and a no-till or turf system (the inter-row). We hypothesize, however, that there may be a radius of influence extending from the perimeter of the strip, causing an ever-decreasing gradient of biophysical effect into the inter-row region.

Project Objectives:

To evaluate the biological diversity of soils under different agricultural management strategies, recognizing that a highly productive agricultural soil is considered to be one with a high degree of biological activity and containing a stable cross section of microorganisms and invertebrates.

To make the best possible estimations of microbial and invertebrate populations and community structure using a range of direct enumeration and community evaluation techniques.

To assess the effect of microbial and invertebrate communities on the following soil physical properties: aggregate stability, bulk density, porosity, and pore size distribution.

To aid in the assessment of soil degradation by identifying soil biological indicators of high soil productivity potential for agricultural soils.

Cooperators

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  • Laura Overstreet

Research

Materials and methods:

This study was established in the fall of 1994 and was initially designed to examine the agronomic effects of alternative management, specifically investigating treatment effects on crop yield and pest and disease pressure. The study had been in place for nine years when this soil biology experiment was initiated. Based on measurable and visible differences in soil physical properties, week community structure changes, and crop yield among treatments, it is believed by the authors that sufficient time has elapsed to allow the biological community to reach a steady state level within each treatment. The field site is located at the Mountain Horticultural Crops Research Station in Fletcher, N.C. The soil type of the field is a Delanco fine-sandy loam (fine-loamy, mixed, mesic, Aquic Hapludult) with 2-7% slopes. The land is gently sloped, moderately well-drained and formed on old alluvial deposits. The site is situated on an old stream terrace subject to infrequent flooding. A randomized complete split-plot design was employed where production treatments (conventional moldboard plow vs. conservation tillage and conventional chemical fertilizer and pesticide inputs vs. organic inputs) were the whole plot treatments and vegetable rotation vs. continuous tomatoes (Solanum lycopersicum) were the split-plot treatment. The three-year vegetable rotation split-plot treatment (see Schematic 2.1) consisted of sweet corn (Zea Mays)/fall cabbage (Brassica oleracea), cucumber (Cucumis sativus )/fall cabbage, and tomatoes for years 1-6 (two full 3-year rotations) and peppers (Capsicum annum); yellow squash (Cucurbita spp.)/ fall broccoli (Brassica oleracea); and staked tomatoes for years six through nine, respectively. The continuous tomato treatment was planted in staked fresh market tomatoes every year (same variety as year three in rotation treatment). All production treatments were trickle irrigated. There were 16 main plots consisting of four treatments and four replicates. Each plot measured 12.2 by 24.4 m (0.03 hectares). There was an area of at least 12.2 m separation between plots to minimize fertilizer and pesticide drift and pest and pathogen migration between plots. The cover crop planted in all treatments was a combination of wheat and crimson clover (Trifolium incarnatum). History of previous cover crops is given in Schematic 2.1. Biomass was measured in the fall using quarter meter square frames to cut all biomass in the given area, which was then dried and weighed. In the conventionally tilled (“plowed”) treatment, the cover was plowed under one month before planting and then disked twice. After final disking in late May, rows were bedded and plastic applied. In the conservation tillage treatments, the cover crop was flail-chopped in the organic treatments and killed with glyphosate in the synthetic treatments. The conservation tillage treatments (both organic and synthetic) were then strip-tilled with a Bush Hog RoTill (producing 30 to 45 cm wide strips) and transplants were planted by hand. Seeded crops were planted with a John Deere MaxEmerge no-till planter. Crops were seeded or transplanted during the second or third week in May. The Bush Hog RoTill implement has a less aggressive ripper and operates at lower horsepower compared to conventional moldboard plowing. This system uses a subsoil shank which rips directly under the row. By lifting the soil up and dropping it, it loosens compaction without inverting the soil or disrupting structure to the same extent as moldboard plowing. It is also less disruptive to soil structure than other implements commonly used for strip tillage, generically referred to as roto-tillers. Fertilizers were applied on the same date for all treatments. In the synthetic fertilizer and pesticide treatment (“Synthetic”), N was applied as NH4NO3 (168 kg N/ha). Nitrogen was applied as a band in both the strip-till row (conservation tillage treatment) and on the soil surface in the area where the plastic was to be applied (plowed treatments). Soil test recommended phosphorus and potassium was surface broadcast over the entire plot each fall when the winter cover crop was planted. In the synthetic treatments, P was applied as triple super phosphate (0-46-0) and K was applied as KCl (0-0-6). Black plastic was applied after fertilization (plowed/chemical and plowed/organic treatments) and fumigated with methyl bromide (plowed/chemical treatment only). After one week, the plastic was punctured where plants were going to be inserted and gases were allowed to escape. Disease and insect control in the synthetic treatments was applied as needed, with materials recommended from the NC Ag Chemical Manual.
In the organic treatments (both plowed and conservation tillage) N, P, and K fertilizers were applied as follows: soybean (Glycine max) meal at 168 kg N/ha (assuming 100% availability during the growing season) was surface applied in the row before planting in the strip-till and banded in-row before black plastic was applied in the plowed treatments. Rock phosphate (0-30-0 assuming 3% solubility) and SoPoMag (0-0-22) were surface broadcast at winter cover crop planting as recommended by soil test reports. Disease and insects were controlled with materials approved for organic production and weeds were controlled by mowing and hoeing. Although the plowed/organic treatment was not fumigated, it was bedded and covered with black plastic at the same time as the plowed/chemical treatment. Strip-till/organic treatment plots were mowed continuously throughout the summer to reduce weed competition around the borders and between rows. This should be considered as an additional labile source of nutrients for plants and soil organisms that other treatments did not receive.

Soil samples were taken for microbial biomass in the spring and fall of 2003, phospholipid fatty acid analysis in the spring of 2003, and earthworm and nematode populations in the spring and fall of 2004. Samples for the enzyme assay were taken in the spring of 2005. Samples for microbial biomass, phospholipids analysis, nematode analysis, and an enzyme assay were taken from respective treatments using a 6-cm diameter push probe. All soils were sampled to a 15-cm depth from within the crop row. Samples were taken by compositing cores taken from four locations (one core per location) along the length of two rows (alternatively on either side of the row) within each subplot. Samples were gently sieved through a 6 mm screen. Soil samples collected for microbial biomass and the enzyme assay analyses were maintained at 4˚ C and all related analyses were initiated within two weeks of the sampling date. Samples reserved for phospholipid analysis were frozen at -14˚ C and freeze-dried prior to analysis. Samples used for nematode analysis were maintained at room temperature until nematodes were extracted. Extractions occurred within a week of the sampling date. Total soil C and N was analyzed by whole soil combustion using the Perkin-Elmer PE 2400 CHN Elemental Analyzer. Plant-extractable soil P was determined using a Mehlich-3 solution extraction of soil samples (Mehlich, 1984). Mehlich-3 extractant is 0.2N CH3COOH + 0.25N NH4NO3 + 0.015N NH4F + 0.013N HNO3 + 0.001M EDTA. A 1:10 soil:extractant ratio (volume basis) was used to extract plant-available soil P by shaking soil/extractant solution for five minutes at 200 revs/minute at room temperature followed by filtration through no. 42 or similar grade filter paper. Filtrate is analyzed for P by ICP. Microbial community structure was examined using microbial biomass C (MBC), microbial biomass N (MBN), phospholipid fatty acid (PLFA) analysis, and an enzyme assay. The microbial biomass analyses (MBC and MBN) provide a means to examine total biomass values and to compare them among treatments. Samples were taken in spring (19 May) and fall (13 October) of 2003. Microbial biomass analyses were conducted by the chloroform fumigation and extraction technique (Hu et al., 1997; Vance et al., 1987) employing a KcC-factor [microbial C-extraction efficiency] of 0.33 after Sparling and West (1988). The MBN procedure was conducted using the alkaline persulfate oxidation digestion method of Cabrera and Beare (1993) with a KcN-factor [microbial N-extraction efficiency] of 0.54 as per Brookes et al. (1985). Microbial biomass C to N ratio was also calculated and used as an indicator of microbial composition (Hu et al., 2001). All values were calculated by subtracting a non-fumigated control value from the fumigated measurements. Phospholipid fatty acid (PLFA) analysis allows evaluation and estimation of major microbial taxonomic groups present in each sample, such as gram positive and negative bacteria, actinomycetes, and fungi. Ibekwe et al. (2001) determined that PLFA analysis has a high degree of sensitivity in monitoring the effects of fumigants on soil community composition and structure. PLFA also allows estimation of taxonomic microbial community diversity. Soil samples were taken for fatty acid analysis in May of 2003 prior to planting of the spring crops and the PLFA procedure was conducted according to an adapted method from Bossio et al., 1998.
The enzyme assay was conducted using four enzymes, selected for discrimination of C, N, and P cycling in agricultural management systems: b-glucosaminidase, b-glucosidase, acid phosphatase and alkaline phosphatase. Soil samples for the enzyme assay were collected in May 2005 prior to planting of spring crops. Samples were air-dried in a forced-air oven at 28˚ C over night, and gently sieved to pass through a 2-mm sieve. Samples were stored in a cold room at 4˚ C until analyzed. Analyses were completed within three weeks of being collected from the field. b-glucosaminidase was analyzed according to the method of Parham and Deng (2000). b-glucosidase was determined using the method of Eivazi and Tabatabai (1988). Acid and alkaline phosphatases were determined individually according to the methods of Tabatabai and Bremner (1969) and Eivazi and Tabatabai (1977). All enzyme analyses were based on measurement of color intensity after enzymatic activity catalyzed the hydrolysis of the substrate from r-nitrophenol. The r-nitrophenol color reagent was measured spectrophotometrically (Shimadzu UV2101PC, UV-Vis scanning spectrophotometer) in the soil filtrate and calculated from a calibration curve prepared as per Tabatabai, 1994.
In addition to looking at the established treatments, soil samples were also collected from the plow/synthetic treatment before and after soil fumigation to examine the effect of fumigation on enzyme activity. Samples taken prior to fumigation were held two weeks longer than other samples before analyzing, since the soil was fumigated approximately two weeks prior to the time that field samples were collected for enzyme analysis. This time difference may affect results obtained from the non-fumigated samples, even though samples were maintained at 4˚ C in the two-week interim period in an effort to reduce biological activity. Invertebrate community structure was performed by identifying and analyzing representative communities of nematodes and earthworms. Nematode community structure was conducted by extracting nematodes using the method of soil elutriation followed by sugar flotation (Barker, 1985). In this procedure a semiautomatic (?) elutriator was used for extracting nematodes and soil fragments from soil. Following this, colloids were removed from samples with a flocculating agent, leaving nematodes suspended in a (0.7 to 1.0 M) sucrose solution. Nematode counts were estimated as per Imbriani (1985) and differential counts were referenced by trophic groups (Freckman and Baldwin, 1990; Yeates et al., 1993). Trophic groups are a “high-order resolution of nematode biodiversity” (Niles and Freckman, 1998) and include the following five functional groups defined by the species feeding habits: bacterial-feeders, fungal-feeders, omnivores, predators, and plant-feeders (both underground herbivores and plant-parasites).
Earthworm estimation was performed using the mustard method of Gunn (1992) as further modified by Högger (1993). A mustard suspension of 0.33% mustard powder was found to successfully extract earthworms. Plastic rings 0.0625 m2 in diameter and approximately 20 cm high were placed at a soil depth of approximately 5 cm and were used in eight locations within each plot (four locations per subplot), for a total area of 0.25 m2 per subplot replicated four times per treatment. Each extraction procedure was repeated two to three times, i.e. 1250 mL of mustard solution was added to each ring two or three times to obtain as many earthworms as possible and each pouring was timed so that there was approximately five minutes between repetitions or until all solution had entered the soil. The number of repetitions was based on the number of earthworms previously extracted. If no earthworms were extracted after the second pouring of mustard solution, we determined that no further repetitions were necessary, but if earthworms were extracted from the first and/or second repetitions, we poured the solution into the rings a third time. Earthworms were collected in about 15 mL of water and stored in a cold room at approximately 4° C for later identification and measurement of weight and length in the lab. Soil samples were taken for microbial activity in the spring (19 May) and fall (13 October) of 2003, physical properties in the fall of 2003, and earthworm and nematode populations in the spring and fall of 2004. Soil samples were taken for phospholipid fatty acid (PLFA) analysis in the spring of 2003. Schematic 3.1 describes the measurements made for microbial characterization of soil properties and also describes measurements made to examine differences in soil physical properties and resource availability for microorganisms and soil fauna.
Samples for microbial biomass, phospholipids analysis, nematode analysis, and the enzyme assay were taken from respective treatments using a 6-cm diameter push probe. All soils were sampled to a 15-cm depth from within the crop row approximating the area of greatest microbial activity in the root zone. Samples were taken by compiling cores from four locations along the length of two rows (alternatively on either side of the row) within each subplot. Samples were gently sieved through a 6 mm screen. Soil samples analyzed for microbial biomass and the enzyme assay were maintained at 4˚ C and analysis was conducted within two weeks of the sampling date. Samples reserved for phospholipid analysis were frozen at -14˚ C and freeze-dried prior to analysis. Samples used for nematode analysis were maintained at room temperature until they were extracted. Extractions occurred within a week of the sampling date. Microbial community structure was examined using microbial biomass C (MBC), microbial biomass N (MBN), phospholipid fatty acid (PLFA) analysis, and an enzyme assay. The microbial biomass analyses (MBC and MBN) provide a means to examine total biomass values and to compare them among treatments. Microbial biomass analyses were conducted by the chloroform fumigation and extraction technique (Hu et al., 1997; Vance et al., 1987) employing a KcC-factor [microbial C-extraction efficiency] of 0.33 after Sparling and West (1988). The MBN procedure was conducted using the alkaline persulfate oxidation digestion method of Cabrera and Beare (1993) with a KcN-factor [microbial N-extraction efficiency] of 0.54 as per Brookes et al. (1985). Microbial biomass C to N ratio was also calculated and used as an indicator of microbial composition (Hu et al., 2001). All values were calculated by subtracting a non-fumigated control value from the fumigated measurements. Microbial activity was assessed using potentially mineralizable C and N analyses. Enzyme analysis was also considered a measurement of optimal microbial activity. Mineralizable C (respiration) was measured using the NaOH base trap technique over 28 days. Twenty grams of field moist soil were measured into small wide-mouth glass vials and set in the bottom of air-tight jars. Ten mL of deionized water were pipetted into the bottom of the jar to maintain relatively high humidity and to prevent loss of moisture from soil. Base traps were designed by suspending 50-mL beakers from the top of the air-tight jars using copper wires and eye-hooks drilled into the top of the jars and sealing them with silicone glue to prevent gas leakage. Base traps were suspended from the lid in an effort to prevent the traps from partially obscuring the mouth of the soil container, which may affect the evolution of carbon dioxide from the jar. Five mL of 0.2 M NaOH were added to each base trap; lids were carefully closed and the time of lid closure was recorded and considered the official start time for the respiration. Traps were collected and titrated on days 0, 7, 14, and 28 with 0.1 M HCl. Sodium hydroxide acts as a trap for CO2 according to the following reaction:
NaOH + CO2 à Na2CO3 + H2O. By adding a known volume of NaOH with a known concentration (0.2 M, in this case), the number of moles of NaOH present at the beginning of the experiment can be calculated. Provided that there is an excess of NaOH provided (ie, there is unreacted NaOH remaining at the end of the study), the excess NaOH can be titrated with a determinable volume of HCl at a known concentration (0.1 M, in this case), so that a calculation yielding the remaining moles of NaOH present can be determined. By subtracting from the original number of moles of NaOH present, the amount required to react with CO2 can be determined. This can then be used to calculate the number of moles of CO2 evolved during a given time period from a measured soil mass. Moisture content was calculated at the beginning of the experiment and all measurements were calculated on an oven-dry soil mass basis.
Potentially mineralizable N was evaluated on the same time increments as for respiration by measuring N concentration in unamended soils over the course of 28 days. Air-tight jars were used and approximately 10.5 grams (fresh weight) of soil was weighed into each of four scintillation vials and all four vials were placed in the air-tight jars. Ten mL of deionized water were added to the bottom of the air-tight jar to maintain relatively high humidity, and thus maintain moisture content of soil. On the extraction dates, one of the four vials was removed from each jar and the soil was extracted and re-weighed into plastic snap-top vials. Potassium sulfate (0.5M) solution was used to extract the potentially mineralizable N from the soil by shaking on a rotary oscillator for 30 min. Samples were filtered and frozen until they were analyzed for NO3- and NH4+ concentrations using a calorimetric N analyzer fitted with a cadmium-copper reduction column (Lachat N Analyzer; Lachat Instruments, Milwaukee, WI). Temperatures were recorded daily for both the respiration and mineralization studies and corrections were made to assure that broad fluctuations in temperature did not occur.
The PLFA procedure was conducted according to an adapted method from Bossio et al., 1998.
The enzyme assay provides an estimation of the functional microbial diversity existing in each treatment by estimating the presence of five soil enzymes: b-glucosadase, b-glucosaminidase, acid phosphatase, and alkaline phosphatase. Soil samples were collected in May 2005 prior to crop planting. Samples were air-dried in a forced-air oven at 28°C over night, and gently sieved to pass through a 2-mm sieve. Samples were stored in a cold room at 4° C until analyzed. Analyses were completed within three weeks of being collected from the field. These assays were conducted using the methods of Eivazi and Tabatabai (1988) for b-glucosidase, Parham and Deng (2000) for b-glucosaminidase, and Tabatabai and Bremner (1969) and Eivazi and Tabatabai (1977) for acid and alkaline phosphatases. All enzyme analyses were based on measurement of color intensity after enzymatic activity catalyzed the hydrolysis of the substrate from ρ-nitrophenol. The ρ-nitrophenol color reagent was measured spectrophotometrically (Shimadzu UV2101PC, UV-Vis scanning spectrophotometer) in the soil filtrate and calculated from a calibration curve prepared as per Tabatabai, 1994. Invertebrate community structure was performed by identifying and analyzing representative communities of nematodes. Nematode community structure was conducted by extracting nematodes using the method of soil elutriation followed by sugar flotation (Barker, 1985). In this procedure a semiautomatic (?) elutriator is used for extracting nematodes and soil fragments from soil. Following this, colloids are removed from samples with a flocculating agent, leaving nematodes suspended in a (0.7 to 1.0 M) sucrose solution. Nematode counts were estimated as per Imbriani (1985) and differential counts were referenced by trophic groups (Freckman and Baldwin, 1990; Yeates et al., 1993). Trophic groups are a “high-order resolution of nematode biodiversity” (Niles and Freckman, 1998) and includes the following five functional groups defined by the species feeding habits: bacterial-feeders, fungal-feeders, omnivores, predators, and plant-feeders (both underground herbivores and plant-parasites).
Soil physical properties were conducted in the fall of 2003. Soil bulk density, pore size distribution, and total porosity were determined by taking four Uhland core samples from each subplot, for a total of eight samples per main plot. The Uhland cores were taken to a temperature- and pressure-controlled laboratory facility equipped with a specially-designed soil moisture release curve apparatus. This facility was used for determination of macro- and microporosity, which was summed to get total porosity. The Uhland cores were first saturated with water for 24 hours in ceramic flat-bottomed stoppered funnels fitted with porous pressure plates. After 24 hours, the funnels were drained of all excess water by siphoning water from around the cores. The funnels were then fitted with air tight seals and the bottoms of the funnels were unstoppered to allow water to move through into collecting cylinders, placed below. Sixty centimeters of positive pressure were applied to the funnels through hoses fitted to the air-tight lids. This pressure was applied for 24 h and the amount of water released during that interval was measured from the collecting cylinders below each funnel. This measurement indicates the water removed from macropores. The cores were then weighed and placed in a drying oven overnight at 105° C. Cores are re-weighed and the moisture retained after the pressure treatment is taken to indicate microporosity. Macroporosity and microporosity are summed to give total porosity. Bulk density is determined independently by determining oven dry weight and dividing by volume of the core. Aggregate stability was determined using a wet-sieving method. Four sieves with hole diameters of 4.75, 2, 1, 0.5, and 0.2 mm were nested from largest to smallest. Fifty g of air-dry aggregates (pre-sieved between 4 and 8 mm) were placed on the top sieve. The nest of sieves was placed in a metal harness and the harness was slowly immersed inside a Plexiglas column filled with deionized water. The harness was secured to a vertical-movement agitator arm directly above the Plexiglas column and was allowed to slake for five minutes. Samples were left to vertically oscillate for 10 minutes. Water was siphoned out of the cylinder and sieves were removed and placed in a drying oven overnight at 105° C. Dry weight of sample retained on each sieve was calculated. Prior to discarding samples, the soil retained on the 4.75 and 2 mm sieves was rinsed forcefully with a jet of water and forceful rubbing to determine if any small stones or gravel were retained. Any such material was dried, weighed, and subtracted from the total sample. Aggregate stability was expressed on a geometric mean weight diameter basis. Geometric mean weight diameter is a soil aggregation index which characterizes the degree of aggregation of a soil sample by calculating a single number. This method of expressing soil aggregation is an alternative to calculating the percentage of a sample’s mass found in each of a series of aggregate size fractions after wet sieving. The disadvantage of expressing aggregation as a series of aggregate stability size classes is that one cannot use these values as a measure of whole soil structure. The geometric mean weight diameter characterizes the structure of the whole soil by integrating the aggregate size class distribution into a single value (Six et al., 2000). The calculation for geometric mean weight diameter is:

Geometric Mean Weight Diameter (GMD) = exp[(Σwilogxi)/Σwi]

where xi is the mean diameter of aggregate class i and wi is the mass of aggregates having mean diameter xi.

Soil samples were collected on July 12-14, 2004. Bulk density was determined by taking an Uhland core sample from each of the three field locations (in the strip, edge of strip, interrow) in each subplot (rotation treatment). Uhland cores were stored at room temperature until bulk density was determined. Soil samples were collected in plastic bags using a 6-cm diameter push probe to a depth of 15 cm from respective field locations by taking four cores from representative areas of each location and compositing the cores. Samples were gently sieved through a 6-mm screen and stored at 4° C until respiration, mineralization, and biomass studies were initiated. All activity measurements were initiated within 10 days after soils were sampled. Total soil C and N was analyzed by combustion of whole soil that has been oven dried and ground to pass through a 1-mm sieve using the Perkin-Elmer PE 2400 CHN Elemental Analyzer. Plant-extractable soil P was determined using a Mehlich-3 solution extraction of soil samples. Mehlich-3 extractant is 0.2N CH3COOH + 0.25N NH4NO3 + 0.015N NH4F + 0.013N HNO3 + 0.001M EDTA. A 1:10 soil:extractant ratio (volume basis) was used to extract plant-available soil P by shaking soil/extractant solution for five minutes at 200 revs/minute at room temperature followed by filtration through no. 42 or similar grade filter paper. Filtrate is analyzed for P by ICP.
The microbial biomass analyses (MBC and MBN) provide a means to examine total microbial biomass values and to compare them among treatments. This analysis was conducted by the chloroform fumigation and extraction technique (Hu et al., 1997; Vance et al., 1987) employing a KcC-factor [microbial C-extraction efficiency] of 0.33 after Sparling and West (1988). The MBN procedure was conducted using the alkaline persulfate oxidation digestion method of Cabrera and Beare (1993) with a KcN-factor [microbial N-extraction efficiency] of 0.54 as per Brookes et al. (1985). Microbial biomass C to N ratio was also calculated and used as an indicator of microbial composition (Hu et al., 2001). All values were calculated by subtracting a non-fumigated control value from the fumigated measurements. Microbial activity was assessed using soil CO2 respiration and potentially mineralizable N analyses. Soil respiration (mineralizable C) was measured using the NaOH base trap technique over 24 days. Ten mL of 0.2 M NaOH were added to each base trap and lids were carefully closed to begin the incubation study. Traps were collected and titrated on day 24 with 0.1 M HCl. The mechanism for the CO2 base trap method and the design of the base trap was previously described in chapter 2 on page 90. Moisture content was calculated at the beginning of the experiment and all measurements were calculated on an oven-dry soil mass basis.
Potentially mineralizable N was evaluated by measuring N concentration in unamended soils over the course of 28 days. Air-tight Mason jars were used and approximately 10.5 grams (fresh weight) of soil were weighed into a scintillation vial and placed in the air-tight jars. Ten mL of deionized water were added to the bottom of the air-tight jar to maintain relatively high humidity and initial moisture content of soil. On the extraction date, the vial was removed from the air-tight jar and the soil was extracted and re-weighed into plastic snap-top vials. A 0.5M K2SO4 solution was used to extract the potentially mineralizable N from the soil by shaking on a rotary oscillator for 30 minutes. Samples were filtered and frozen until they were analyzed for nitrate and ammonium concentrations using a N analyzer fitted with a cadmium-copper reduction column (Lachat N Analyzer; Lachat Instruments, Milwaukee, WI). Temperatures were recorded daily for both the respiration and mineralization studies and corrections were made to assure that broad fluctuations in temperature did not occur. Temperatures ranged from 24° to 30° C during the course of these experiments.

Analysis of variance was conducted to examine relationships between individual biological measurements and respective treatments using SAS software (SAS systems, release 8.02, SAS Institute, Inc. Cary, NC). Phospholipid fatty acid measurements were analyzed by principal components analysis (PCA) and redundancy analysis (RDA) on a mole percent basis using CANOCO software version 4.5 (Microcomputer Power, Inc., Ithaca, NY) and SAS software. Data was standardized by dividing fatty acid values by the standard deviation and centering the values. Principal components were thus based on the correlation matrix since the standard deviations of some of our individual fatty acids were large compared to the means. Sample data was not standardized or centered. The results of our data were determined with the Sherlock MIS system using the ‘eukary’ method as a quick, inexpensive, and reproducible method for describing soil microbial community structure (Ibekwe and Kennedy, 1999). Data is presented as 2D plots for greater understanding of relationships. Analysis of variance was further conducted on the first and second principal components using SAS PRINCOMP to determine the relationships among measured variables. RDA uses the environmental variables (soil physical properties, in this case) to explain the species data (the biological properties, in this case). The RDA value for each sample is obtained by a multiple regression of biological parameters for that sample on the physical property values for that sample, which is used to create a correlation matrix of all combinations of variables (ter Braak and Šmilauer, 2002). We tested the significance of the ordination axes as explanatory variables for our biological data by performing the Monte Carlo permutation test using the Canoco software program. The Monte Carlo permutation is an objective test for evaluating the statistical significance of the eigen values produced in ordination procedures by testing the significance of the relative percent variance accounted for by each eigen value when the data comes from a sample rather than from entire population (McGarigal et al., 2000). It is a type of statistical resampling procedure commonly referred to as bootstrap procedures. The advantage of this nonparametric test is that the only assumption required is that the observations are independent; there is no requirement or assumption regarding the distributional properties of a sample drawn from this resampling technique (McGarigal et al., 2000).

Research results and discussion:

Soil C and N concentration for spring and fall sampling dates were lower in plots that had been plowed annually and amended/treated with chemical fertilizers and pesticides compared to those plots that had been strip-tilled and amended/treated with organic fertilizers and pest control. Neither rotation nor any of the rotation interaction terms were significant, so the split-plot treatment was averaged over main plot treatment values and the results are shown in figures 2.1 and 2.2. Analysis of variance revealed that soil C and N were both significantly affected by tillage for spring and fall sampling dates (P < 0.05) (Table 2.1, Figures 2.1 and 2.2). Input was also a significant treatment effect for total C in the fall sample and for total N in the spring sample.
Tillage strongly reduces soil C content compared to pasture or other undisturbed soil environments by accelerating CO2 emission (Beare et al., 1994; Tate, R.L., 2000; Schimel et al., 1985; Elliott, 1986; Burke et al., 1989; Woods, 1989; Conant et al., 2001). Al-Kaisi and Yin (2005) report that CO2 emission is enhanced under conventional tillage by increasing soil aeration, increasing soil-crop residue contact, enhancing plant nutrient availability (Logan et al., 1991; Angers et al., 1993), and increasing exposure of soil organic C in inter- and intra-aggregate zones to allow rapid microbial oxidation (Reicosky and Lindstorm, 1993; Beare et al., 1994). Elliott (1986) suggests that the large loss of soil organic matter due to long-term cultivation results from the disruption of roots and fungal hyphae as well as the organic C associated with the fine clay fraction. Losses in organic N are also related to tillage as a result of reduction in soil structure and increased soil erosion (Chaney and Swift, 1984). Several studies have concluded that organic C and N are more important for the stabilization of soil structure than soil P (Chaney and Swift, 1984; Tisdall and Oades, 1982).
Extractable soil P levels were greater in strip-tilled plots than in plowed plots (Figure 2.3), but tillage effect was significant only during the fall sampling date (Table 2.1). The higher levels of P observed in the strip-tilled treatments were probably a result of the greater levels of organic P resulting from the higher organic C content observed under strip-tillage, since these two factors are highly correlated in most soils (Havlin et al., 1999). Soil pH values were measured in water and found to be between 5.5 and 7.0, which should not cause P to precipitate as Al, Fe, Ca, or Mg phosphates in any of the treatment plots. There was a trend for spring and fall sampling dates in which treatments receiving organic inputs displayed greater extractable soil P values in the vegetable rotation treatments compared to the continuous stake tomato treatments, and conversely, all synthetic-input treatments displayed greater extractable P values in the continuous stake tomato treatments compared to the rotation. This produced a significant input by rotation interaction, even though the rotation treatment itself was not significant (Table 2.1). The trend was more evident in the strip-tilled treatments compared to the plowed treatments.
Cover crop biomass was quantified in 2003. In plowed treatments, the biomass was measured in late April before the cover crop was plowed under; in strip-till plots, biomass was not measured until mid-May when covers were killed (synthetic) or flail-chopped (organic). Plant biomass was significantly greater in the strip-till treatments than the plowed treatments, as expected (Table 2.1). Plant biomass was also greater in the rotated treatments compared to the continuous stake tomatoes (Table 2.1). Significant interaction effects were also present for the rotation-tillage and rotation-input (Table 2.1). The rotation effect and interactions may be explained by the additional fertilizer that is added to the rotation plots in years when a fall crop is planted, thus providing improved fertility to the cover in the same plots.
Spring samples were taken prior to fumigation of the plow/synthetic treatment and before the strips were established in the strip-till/organic and strip-till/synthetic treatments. The plowed treatments (plow/organic and plow/synthetic) had been plowed once for the initial bed preparation approximately two weeks before the spring samples were taken. Because microbial biomass C and N were unaffected by rotation (Table 2.3), this variable was averaged over whole plot treatments (tillage and inputs).
Comparing treatment effects (Table 2.2) indicates that the tillage and input treatments were significant factors in MBC and MBN for both spring and fall sampling dates. Microbial biomass C results (Table 2.3) indicate that the strip-till/organic treatment had greater biomass C than any other treatment at both the spring and fall sampling dates. In the spring, the plow/organic treatment had the second greatest MBC. By the fall sampling date, MBC in the plow/organic treatment decreased 29% from the spring value, while the strip-till/organic treatment increased 14% and the strip-till/synthetic treatment remained nearly the same. The plow/synthetic treatment was lower than any other treatment for MBC at both sampling dates and also displayed a reduction in average MBC of 51% from spring to fall dates. In the spring, the MBC in the plow/synthetic treatment was less than 40% of the value of the MBC in the strip-till/organic treatment, and in the fall, the value was only 17% of the strip-till/organic treatment.
These results indicate that tillage and input significantly influence total microbial biomass production. Bossio et al. (1998) found that organic systems with high organic matter inputs sustained increases in microbial biomass. The results produced in this study concur with those of Bossio et al. (1998) in that the treatments receiving strip-tillage and organic amendments (greater organic matter inputs relative to other treatments) demonstrated greater microbial biomass values. Considering results of similar studies, it is not unreasonable to believe that microbial biomass was increased in the strip-till and reduced in the plowed treatments over the course of the crop season as a result of the greater availability of high-quality C inputs from the cover crop and weed residues remaining on the soil surface in the strip-tilled and organic treatments (Wardle et. al., 1999; Emmerling et al., 2001).
In this study, the tillage activities for the plow treatments are implemented with an initial moldboard plow tilling event in the first or second week of April. The final plowing and diskings of the plots occurs in the second or third week of May. The strip-till treatments are also tilled in the row during the second or third week of May; all fertilizers are also applied at this time, immediately prior to transplanting or seeding crops. The spring samples for biological activity and biomass were taken during the second week of May, just before the final tillage activities and fertilizer application. Therefore the spring sampling date measures microbial biomass and activity before the spring tillage and fertilizer applications, and thus tests effects from the previous season’s management applications. The fall sampling occurred in the second week of October, and tests the season-long effects of the agricultural management applications from the current year.
A total of twenty-eight fatty acids were extracted and identified using fatty acids standards and MIDI peak identification software (MIDI, Inc., Newark, DE). Ten fatty acids were consistently found in all samples, which is a low value relative to other studies which routinely extract 20 to 30 fatty acids in a soil sample. Perhaps this is the result of the experimental area being used in a long-term agricultural function, and therefore having a lower microbial diversity than soils from pastures or grasslands. This may also be a result of the extraction procedure or peak analysis procedure used. When our samples were tested using a different peak identification program, we determined that there was 75-80% agreement between protocols, with the test program giving us on average about five more peaks per samples than our original analysis, but with much higher variance among repetitions. In order to maintain a high level of sensitivity we opted to maintain our original peak identification program.
The ten fatty acids consistently found in all samples were: a15:0, 16:0, i16:0, 16:1w5c, 18:0, 18:1w7c, 18:1w9c, 10Me16:0, 10Me18:0, and 19:0 cy w8c. These ten fatty acids comprised an average of 71% of the total fatty acid mole percents for each sample, with the smallest average value being 56% of the total fatty acids. These fatty acids have been identified as follows: a15:0 and i16:0 are found primarily in gram-positive bacteria (Ratledge and Wilkinson, 1988; Paul and Clark, 1996); 18:1w7c has been isolated from gram-negative bacteria and aerobic eubacteria (Paul and Clark, 1996); cyclopropane fatty acids (e.g., 19:0 cy w8c) are commonly found in bacteria, but not in actinomycetes (Lechevalier and Lechevalier, 1988); 16:1w5c is predominantly found in fungi, especially arbuscular mycorrhizal fungi (Paul and Clark, 1996); 18:1w9c has been isolated from fungi and gram-positive bacteria as well as from higher plants (Zelles, 1997; Paul and Clark, 1996); and 10Me16:0 and 10Me18:0 are identified as being present in actinomycetes. The fatty acid 16:0 has been found to be present in almost all microorganisms and cannot be considered specific to any particular taxa of soil organisms. The fatty acid 16:0 has also been found to correlate well with total microbial biomass C (Ratledge and Wilkinson, 1988). The fatty acid 18:0 is not currently identified with specific microorganisms.
For PLFA profiles, the mole percent of individual fatty acids was used to analyze data. The first principal component (PC1) accounted for 46.6% of the total sample variance in the multivariate fatty acid response (Figure 2.5). Principal component two (PC2) was found to account for an additional 22.7% and principal component three (PC3) encompassed an additional 13% of the variance, to give a combined variance of 82.2% within the fatty acid dataset for the first three principal components (Figures 2.5 and 2.6). Figure 2.5 (PC1 and PC2) shows that the individual treatment replications (circles identified by numbers on the biplot) tend to occur together on the plot. Figure 2.6 (PC2 and PC3) gives a greater separation of treatment values into clusters. This tendency for treatment replications to cluster indicates that there is less within-treatment variance than between-treatment variance; thus, field variability represented in replications in this study did not have a strong effect on differences in the observed fatty acid profiles (Bossio et al., 1998). Also apparent from the biplots is the tendency of intermediately intensive treatments 2 and 3 (plow/organic and strip-till/synthetic inputs, respectively) to occur together, indicating greater similarity between these treatments relative to the more intensive management treatments (treatment numbers 1 and 4). Treatments 1 and 4 are oriented opposite each other along principal component 2 and principal component 3 with the intermediate treatments (2 and 3) found between them. Bossio et al. (1998) had similar results consistent with differences in organic C inputs between organic and conventional farming systems, having significantly different (p<0.05) PLFA profiles on six out of seven sampling dates taken throughout the growing season.
The first principal component in this analysis does not clearly differentiate among the treatments, as indicated by the occurrence of most treatments about the x=0 hash mark on the PC1 axis. This is confirmed by analysis of variance from the factorial effects model, where the treatment sum of squares was 62.5% of the total sum of squares for PC1. Thus it appears that treatment effects explain much more variation along the second principal component, although the second principal component explains less of the total variance in the fatty acid data set. In the biplot shown in Figure 2.5, treatment 1 is oriented in the positive direction of PC2 while treatment 4 is oriented in the negative direction of PC2 and the intermediate treatments (numbers 2 and 3) are found in between. The analysis of variance corroborates the significance of the treatment effects along the second principal component. The R-square value for this analysis is 0.8986. Principal component three accounts for 13% of the total variance in the principal components analysis of the fatty acid data and regression of PC3 on treatment effects reveals an R-square value of 0.8362.
Since the first principal component accounted for 46.6% of the variance within the fatty acid data, and the linear model of treatment effects and their interactions accounts for 62.5% of the variance found in PC1, then 29.1% (0.625 * 46.6) of the total variation in the data set is explained by the treatment effects revealed in PC1. Likewise, since PC2 accounts for 22.7% of the total variation among fatty acids in this study, and because 90% of the variance is due to treatment effects, 20.43% (0.90 * 22.7) of the total variance among fatty acids is accounted for by the treatment effects revealed in PC2. Analogously, PC3, which accounts for 13% of the total variation in the fatty acid dataset, had an R-square value of 0.8362 when regressed on treatment effects. Therefore, PC3 accounts for 10.86% of the total variance among fatty acids. The total variance among PLFA profiles accounted for by the treatment effects revealed by the first, second, and third principal components is 60.39%. Since we only analyzed the first through third principal components, we can conclude that at least 60% of the variation found in microbial fatty acid composition in this agricultural soil results from the agricultural management techniques implemented in this study.
Principal component one did not differentiate fatty acids or samples to an appreciable degree. When ANOVA was performed on the first principal component using a complete 2X2X2 factorial model with tillage, inputs, and replication as sources of variation, the model was not significant (P = 0.7790) nor were any of the treatment effects. For PC2, tillage and input effects, as well as the interaction term, were significant (p-value = 0.0003, 0.0016, and 0.0207, respectively). The ANOVA for PC2 revealed that the regression model of treatment effects significantly explains fatty acid composition (P=0.0253). The ANOVA on the third principal component was not significant for treatment effects (p=0.1268) for the model and had an R-square value of 0.8362. The input treatment and the input by tillage interaction were significant effects in the model (p-values of 0.0165 and 0.0117, respectively) for PC3. In Figure 2.6, showing the biplot of principal components two and three, PC3 appears to separate the most intensively managed treatment (number 1, tillage/synthetic inputs) from the other treatments. Principal components two and three reveal that the fungal biomarker, 16:1w5c, is closely associated with the least intensively managed treatment (number 4, strip-till/organic inputs). The actinomycetes and bacterial biomarkers, 10Me18:0 and 19:0cy, respectively, are most closely associated with the intermediate treatments (numbers 2 and 3, plow/organic inputs and strip-till/synthetic inputs, respectively). The biomarkers 16:0 and 18:0, which are not clearly affiliated with particular microbial taxa, are most closely associated with the most intensively managed treatment, number 4 (plow/synthetic inputs).
As shown in figure 2.7, the first, second, and third principal components, which account for over 82% of the total sample variance in the correlation matrix of phospholipid fatty acid values, gives the most information and further principal component axes give relatively little additional information. This is why only the first, second, and third principal components were examined in this analysis.
Some researchers have proposed that the proportion of different fatty acids extracted from within different treatments gives an indication of the relative diversity of microorganisms occupying the respective treatments (Wander et al., 1995; Korner and Laczko, 1992). However, this is arguable, since individual fatty acids in a PLFA profile do not represent specific or individual species. This is because an individual microbial species can have numerous fatty acids and because the same fatty acids occur in many different organisms (Lechevalier, 1977). Further, Murata et al. (2002) found in a study quantifying diversity of bacterial fatty acid methyl ester (FAME) profiles from whole soil and from culture media that diversity in FAME profiles was proportional to the amount of bacterial fatty acid extracted. Given our low fatty acid recovery, we concluded that it would not be appropriate to determine the diversity of treatments using our PLFA results. Instead we calculated a simple average of different types of fatty acids based on the whole plot replicates (n=8). The results are as follows: strip-till/organic 16; strip-till/synthetic 15.75; plow/organic 14; and plow/synthetic 10.7. In general, there were more fatty acids extracted from the rotation treatment compared to the continuous stake tomatoes treatment. The one exception was the strip-till/synthetic treatment, where an average of 15.25 fatty acids found from the rotation treatment, and an average of 16.25 fatty acids found from the continuous treatment. The differences between average numbers of fatty acids extracted from the rotation versus continuous treatments were smaller for the strip-tilled treatments compared to the tilled treatments. The greatest discrepancy between the rotation treatments was found in the most intensively managed treatment, where tillage and synthetic inputs were implemented. There were on average an additional 2.67 different fatty acids extracted from the rotation treatment compared to the continuous treatment in the plow/synthetic treatment.
Fumigation did not affect the activity of b-glucosaminidase (p-value = 0.9312), so this treatment was combined with the non-fumigated samples in the analysis of variance (Table 2.4). Tillage and input treatments were both strongly significant in determining b-glucosaminidase activity (p-values of <0.001 for both). There was also a significant rotation-by-tillage interaction, but it was not consistent. As seen in Figure 2.8, the strip/organic treatment had the greatest b-glucosaminidase activity compared to other treatments in the respective rotation treatment level (rotation or continuous). For the ‘rotation’ level of t

Participation Summary

Project Outcomes

Project outcomes:

We found that greatest biological activity among various field production systems was in those systems that had the least tillage. Changing production systems from chemical to organic also produced increased biological activity, but not to the extent that tillage reduction did. We have given many workshops and speaker presentation on the findings of this research, with much interest from producers who want to achieve greater soil productivity from their farm operation.

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.