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Linking soil biotic and abiotic factors to sweet cherry tree establishment in new and old Okanagan Valley… Munro, Paige 2018

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LINKING SOIL BIOTIC AND ABIOTIC FACTORS TO SWEET CHERRY TREE ESTABLISHMENT IN NEW AND OLD OKANAGAN VALLEY ORCHARDS  by  Paige Munro  B.Sc. (Honours), The University of British Columbia, 2015  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCENCE  in  The College of Graduate Studies (Biology)  THE UNIVERSITY OF BRITISH COLUMBIA  (Okanagan)  February 2018  © Paige Munro, 2018       ii The undersigned certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled:  LINKING SOIL BIOTIC AND ABIOTIC FACTORS TO SWEET CHERRY TREE ESTABLISHMENT IN NEW AND OLD OKANAGAN VALLEY ORCHARDS  submitted by Paige Munro in partial fulfilment of the requirements of  the degree of Master of Science.  Dr. Louse Nelson, Biology, Irving K. Barber School of Arts and Sciences Supervisor  Dr. Melanie Jones, Biology, Irving K. Barber School of Arts and Sciences Supervisor  Dr. Thomas Forge, Soil Ecology, Agriculture and Agri-food Canada,  Supervisory Committee Member   Dr. Miranda Hart, Biology, Irving K. Barber School of Arts and Sciences Supervisory Committee Member  Dr. Dave Scott, Earth and Environmental Science, Irving K. Barber School of Arts and Sciences University Examiner        iii Abstract            In the Okanagan Valley of British Columbia, sweet cherry (Prunus avium L.) has traditionally been replanted into soil that previously supported tree fruits. However, growth of young fruit trees replanted into old orchard soil is often poor and thought to be due to plant-parasitic nematodes (i.e. Pratylenchus spp.) and fungi. Due to climate change, cherry production is expanding into northern and higher elevation areas of this region that were not previously cultivated to tree fruits. Models have considered how climate and soil physicochemical properties will influence cherry range expansion, but they have not considered soil biology. The first objective of this study was to compare soil from 18 old (n=12) and new orchard soils (n=6) with respect to the influence of soil biology on cherry growth, by measuring plant growth response to sterilization in a bioassay, and to determine which biotic and abiotic properties best predict cherry growth among this array of orchard soils. Shoot height increment was significantly greater in untreated (non-sterilized) new soil relative to old soil where microbial activity was reduced (sterilized). According to multiple regression, the variables FDA hydrolysis, organic carbon, and sodium were positive predictors of plant growth for both new and old soils. Using greenhouse and field experiments, the second objective of this study investigated how compost and woodchip mulch application affected soil biotic and abiotic properties compared to non-amended soil in two new, northern, and two old, central Okanagan sweet cherry orchards over two growing seasons. In the field study, compost-amended soil resulted in greater soil nutrient status at all four orchards, but there were few effects on soil biological properties. In the greenhouse study, amended soil from both new sites and one old site resulted in lower Pratylenchus root colonization than non-amended soil. Overall, results from these experiments suggested that (1) new orchard soils are ‘biologically suitable’ for planting sweet cherry, and (2)  iv compost application may be a tool to maintain soil health, and mitigate future soil-borne disease in old, replant stress-prone sites, as well as in newly established orchard soils that have never cropped sweet cherry or other tree fruits.                     v Lay Summary            Using greenhouse experiments, I compared cherry plant growth in newly cultivated Okanagan Valley soils to soils that have been used for orchard production for an extended period (>10 years). I evaluated which abiotic and biotic soil factors best predicted growth in these soils. Results indicated that new orchard soils were more suitable for growing plants than most old orchard soils, and plant growth in new and old soils was positively correlated with soil microbial activity, organic carbon, and sodium. Using field experiments, I examined how soil amendments, such as compost and woodchip mulch, affected soil biotic and abiotic properties in two northern Okanagan cherry orchards recently planted into newly cultivated soil and two central Okanagan cherry orchards that have been in production for >10 years. Compost application in new and old orchard soils resulted in greater soil fertility after two growing seasons compared to woodchip- and non-amended soil.             vi Preface             For the first part of this study, soil was sampled from 18 orchards in the Okanagan Valley of British Columbia, Canada, as recommended by Dr. Louise Nelson and Dr. Melanie Jones of University of British Columbia Okanagan (UBCO), and the collaborators Dr. Thomas Forge and Dr. Denise Neilsen of Agriculture and Agri-food Canada (AAFC). Sampling was conducted with permission from orchard owners. I performed the sampling with Dr. Tanja Voegel (UBCO). The soil collected was used to grow cherry plants. I was responsible for analyses of plant growth and soil after harvest. A & L Laboratories (London, Ontario) did the soil chemical analyses.  For the second part of this study, soil amendment and irrigation application strategy trials were established at four Okanagan Valley cherry orchards. Dr. Louise Nelson, Dr. Melanie Jones, Dr. Kirsten Hannam (UBCO), Dr. Thomas Forge, and Dr. Denise Neilsen were responsible for site selection and development of experimental design at the four orchards. Two of the orchards were owned and maintained by Coral Beach Farms Ltd. and were located in Coldstream, BC, and Lavington, BC. One of the orchards was owned and maintained by Dendy Orchards (Kelowna, BC). The last orchard was maintained by AAFC employees, and it was located at the Summerland Research and Development Centre (Summerland, BC). I performed the field work with Tirhas Gebretsadikan (UBCO) and Dr. Tanja Voegel, and we were also responsible for the processing and analyses of samples from all the sites. The soil chemical analyses were performed by A & L Laboratories (London, Ontario) in 2015, and by the BC Ministry of Environment, Technical Services Lab (Victoria, British Columbia) in 2016. I wrote my thesis with the guidance of my supervisors Dr. Louise Nelson and Dr. Melanie Jones, and it was reviewed by my supervisory committee: Dr. Miranda Hart (UBCO) and Dr. Thomas Forge.  vii Table of Contents Abstract ………………………………………………………………………………………………......iii Lay Summary ……………..……………...................................................................................................v Preface ………………………………………………………....…………………………………….…...vi Table of Contents ……………………....………………………………………………………….…….vii List of Tables …………………………………………………………………………………….………..x List of Figures ………………………………………………………………………………….………xvii Abbreviations ...................………………………………………………………………........................xix Acknowledgements ……………………………………………………….....…………………………..xx Dedication ……………………………….......…………………………………………………………..xxi 1.0 Chapter 1: Introduction……................................................................................................................1      1.1 Adapting crop production to climate change ….…………………………………………………....1      1.2 Defining soil quality and soil health ……………….……..……………………………...................2  1.2.1 Assessing soil health ……………………………………………………………………………………...3  1.2.2 Biological properties of soil health assessment .………………………………………………………….3          1.2.2.1 Soil organic matter ……………………………………....……….…………………..………………………..3           1.2.2.2 Microbial biomass …..………………………….......……………………………….…………………..……..5                      1.2.2.3 Microbial activity ………………….…….……......…………………………………………………………...6                      1.2.2.4 Microbial diversity ……………………………………………….....…………………………………………7  1.2.3 Soil physical and chemical properties of soil health assessment ………………………………………...7 1.3 Role of arbuscular mycorrhizal fungi in soil and plant health ............................................................9     1.4 Replant stress of tree fruits ……………………………………….......…………………………….11  1.4.1 Causes of replant stress …………………………………………………………………………………12           1.4.1.1 Plant-parasitic nematodes ……...…………………………………………………………………………….13 1.5 Soil management strategies ………………………………………………………………………..15 1.6 Soil Supressiveness ...........................................................................................................................16 1.7 Organic soil amendments ……..........……..……………………………………………………….18      1.7.1 Factors influencing the effects of organic soil amendments……………………......……………………19  1.7.2 Indicators of disease-suppressive organic amendments ………………………………………………...21 1.8 Background to the study ....................................................................................................................22 1.9 Objectives and Hypotheses ................................................................................................................24  viii 2.0 Chapter 2: Soil biota from newly established orchards are more beneficial to early growth of cherry than biota from older orchards ....................................................................................................29 2.1 Background ........................................................................................................................................24     2.2 Materials and Methods .…………………………………………………………………………….31           2.2.1 Soil sampling ......…….....……………………………………………………………………………….31  2.2.2 Preliminary soil physicochemical property analysis ……………………………………………………33  2.2.3 Experimental treatments and design …………………………………………………………………….34  2.2.4 Plant growth analyses ...………………………………………………………………………………....36  2.2.5 Nematode extraction and quantification from soil and roots …………………………………………...36  2.2.6 Microbial activity in soil of plants …....……………………………………......………………………..38 2.2.7 Statistical analyses ......................................................................................................................................39     2.3 Results …………....………………………………………………………………………………...42          2.3.1 Impact of old and new orchard soils on growth of ‘Crimson’ sour cherry plant …....…….….………....42 2.3.2 Microbial activity in soils from new versus old orchards ……………………………………………….46 2.3.3 Pratylenchus spp. abundance in roots of plants and in soils from different orchard type ………………48 2.3.4 Impact of biotic and abiotic soil health properties on plant growth in new and old orchard soils .............50 2.4 Discussion ………………………………………………………………………………………….54  2.4.1 Impact of old and new orchard soils on growth of cherry plants ..............................................................55  2.4.2 Abiotic and biotic predictors of plant growth …………………………………………………………...55  2.4.3 Pratylenchus spp. abundance in roots of plants and in soils from different orchard types……....….......59           2.4.4 Final chapter remarks ……….………………...……………………….................………......................60 3.0 Chapter 3: The effect of organic amendments on soil health in two new and two old Okanagan Valley sweet cherry orchards over two growing seasons ……………………………………………..62      3.1 Background …...……….......………………………………………….…………..……………….62  3.2 Materials and Methods .....................................................................................................................64        3.2.1 Field experiment with organic amendments in cherry orchards …………….……………………........64                3.2.1.1 Description of research orchard sites …………………………………………………………………........64                3.2.1.2 Experimental design at each orchard site ……………………………………………………………..........66                3.2.1.3 Organic amendment information and application rates …………….………………………………….......67                3.2.1.4 Soil and root sampling …………....……………………………………………..………………………….70                          3.2.1.4.1 Baseline soil sampling  …………………........………………………………………………………………....70                          3.2.1.4.2 Soil sampling in experimental plots …….……………………………………….....…………………………..70                   3.2.1.5 Soil abiotic and biotic property analyses ……………………….…………………………................…….71                                 3.2.1.5.1 Soil physicochemical property analysis ……………………...………………………………………................71                                         3.2.1.5.2 Microbial activity in soil ……………………...…………………………………………………………….......72                                         3.2.1.5.3 Nematode extraction and quantification in soil and roots …………………...…………………………........…72                                         3.2.1.5.4 Percent colonization by arbuscular mycorrhizal fungi ……………………...……………………….................74                                         3.2.1.5.5 Estimation of total fungi and total bacteria in soil ……………………...……………………………................75  ix                                                         3.2.1.5.5.1 Generation of standard curves for qPCR ……………………………………………………..........76                                                         3.2.1.5.5.2 qPCR temperature profiles and reaction mixtures …………………………………….…….....…..77                                                         3.2.1.5.5.3 Calculations to obtain copy number per g dry soil …………….....………………………………..78    3.2.1.6 Statistical analysis …….……………………………………………………..........………………..…………78         3.2.2 Greenhouse bioassay using amended orchard soil …………………………………………………....…80    3.2.2.1 Soil sampling …………………………………………........………………………………..………………..80                3.2.2.2 Soil sterilization …………………………………........….….…………………………….…..………………80     3.2.2.3 Experimental design ………………………………..........……………………………………………………81                     3.2.2.4 Plant growth analyses ……....…………………………………………………………...............……………83                     3.2.2.5 Nematode extraction and quantification in soil and roots ….…………………………………………….......83                      3.2.2.6 Statistical analyses ……………………………………………………………………………………...........84 3.3 Results ...............................................................................................................................................86 3.3.1 Field experiments: Effect of soil amendments on soil biotic and abiotic properties across Sites 1, 2, and 3 ………………………………………………………………………………………….....................................86 3.3.2 Greenhouse experiment of plants grown in soil from Sites 1, 2, and 3 .....................................................91                     3.3.2.1 Effect of soil treatments on plant growth and Pratylenchus abundance in soil across sites 1, 2, and 3 .....…..91 3.3.3 Effect of soil treatments at each site on plant growth and Pratylenchus abundance ……………………93          3.3.3.1 Site 1 ……………………………………….……………………………………………..…………………..93           3.3.3.2 Site 2 ….………………………………………………………………………………..…………………….97                      3.3.3.3 Site 3 ………………....……………………………………………………………………………………..101           3.3.4 Site 4 …………......…………………………………………………………………………………….105                      3.3.4.1 Field experiment: Effect of soil treatments on soil biotic and abiotic properties ……………….…….....…105                      3.3.4.2 Greenhouse experiment: Effect of soil treatments on plant growth and Pratylenchus spp. abundance ........116 3.4 Discussion ………………………………………………………………………………………...119       3.4.1 Effect of the soil amendments on soil biotic and abiotic properties among Sites 1, 2, and 3…….........119                3.4.1.1 Field experiments …………………………………………………..........………………...…………….…119                  3.4.1.2 Greenhouse experiments ………………………………………………………………...…….........……..121   3.4.2 Field and bioassay experiments with soil from Site 4 ............................................................................123   3.4.3 The effect of the amendments on soil biotic and abiotic properties at all four sites ..............................125   3.4.4 Final chapter remarks ... .........................................................................................................................126 4.0 Chapter 4: Final Conclusion .............................................................................................................127 Bibliography ............................................................................................................................................131 Appendices ...............................................................................................................................................151 Appendix A: Additional information for Chapter 2 ………………………………………………….151 Statistical Models in R ……………………………………………………………………………………….154 Appendix B: Additional information for Chapter 3 ………………………………………………….155   x List of Tables Table 2.1 Site information for all orchard soils used in this study, including: name, tree cultivar and rootstock planted, orchard type (old, new or non-cultivated), year current cherry trees were planted, land use history, soil type, and the latitude and longitude. ............................................. 33 Table 2.2 Mean shoot height increment, shoot weight, root weight, and plant weight of each orchard type and sterilization regime (sterilized or non-sterilized) combination. ........................ 42 Table 2.3 Percent difference in growth for plants grown in sterilized soil compared to plants grown in non-sterilized soil from ‘new’ (n=6) and ‘old’ (n=12) orchard types. .......................... 44 Table 2.4 Total weight of plants grown in non-sterile and sterile soil for each site. ................... 44 Table 2.5 Shoot weights of plants grown in non-sterile and sterile soil for each site. ................. 45 Table 2.6 Root weight of plants grown in non-sterile and sterile soil for each site. .................... 46 Table 2.7 Mean FDA hydrolysis (µg g-1) of non-sterile soil from each site (n=5 pots for each site), and each orchard type (nnew=6; nold= 12) after 10 wk of growth of cherry plants. .............. 47 Table 2.8 Populations of Pratylenchus spp. in soil at time of planting (n=1 for each site), and in soil and roots of plants at harvest, as well as total nematodes in soil at harvest, for each site (n=5 for each site) and orchard type (nnew=6; nold=12). Multiplication rate was determined by dividing the final population densities of Pratylenchus in soil (at harvest) by the initial density in soil (at planting). ....................................................................................................................................... 49 Table 2.9 Abiotic soil measurements for each site were measured prior to the bioassay in December 2015. The measurements included: EC (electrical conductivity), pH, % OM (organic matter), P (phosphorus), K (potassium), Mg (magnesium), Ca (calcium), Na (sodium), C: N (carbon-to-nitrogen) ratio, TN (total nitrogen), OC (organic carbon), CEC (cation exchange capacity), and POXC (permanganate oxidizable carbon). Each chemical variable was measured on one composite sample per site. Topographic variables including, elevation and latitude, are also given for each site. ................................................................................................................. 52 Table 2.10 The proportion of the variance of each variable that can be explained by the principal components. Any variables that described < |0.5| of the variation in the data were eliminated before conducting stepwise regression analyses. .......................................................................... 54 Table 2.11 The variables that predicted shoot height increment in new and old orchard soils after conducting stepwise regression analyses. ..................................................................................... 54  xi Table 3.1 Orchard type, plot size, and compost and mulch application characteristics for all four sites. ‘Glengrow’ compost and the woodchip mulches (‘Better Earth’ and Pryce’) were applied to Sites 1, 2, and 3 in Spring of 2015 and 2016, and ‘BigHorn’ compost was applied to Site 4 in Spring of 2015 only. ..................................................................................................................... 68 Table 3.2 Analytical results for ‘Glengrow’ and ‘BigHorn’ composts, and the Douglas-fir woodchip mulches. ....................................................................................................................... 69 Table 3.3 Baseline soil physicochemical analyses taken before experimental plot establishment in June 2015 from soil depths of 0-15 cm and 15-30 cm (n=1 for each parameter). .................... 88 Table 3.4 Effect of soil amendment (bare, compost, or mulch) on cation exchange capacity (CEC), electrical conductivity (EC), and elements phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) among Sites 1, 2, and, 3 in October 2015 and 2016 sampling years.  ............................................................................................................................................ 89 Table 3.5 Effect of soil amendment (bare, compost, or mulch) on permanganate-oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio, and pH among Sites 1, 2, and 3 in October 2015 and 2016 sampling years.  ............................................................................................................. 89 Table 3.6 Effect of soil amendments (bare, compost, or mulch) on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S copy number) and fungi (18S copy number), fungal-to-bacterial (F: B) ratio, and % arbuscular mycorrhizal fungi (AMF) colonization among Sites 1, 2, and 3 in October 2015 and 2016 sampling years. ..................................................................... 90 Table 3.7 Effect of soil amendment (bare, compost, or mulch) on Pratylenchus spp. abundance in 1 g root and 50 g bulk soil, and total nematodes in 50 g bulk soil among Sites 1, 2, and 3 in October 2015 and 2016 sampling years.  ...................................................................................... 90 Table 3.8 Analyses of variance (ANOVA) results for the effects of the main factors amendment (compost, bare, or mulch) and sterilization regime (sterilized or non-sterilized), and the random factors site and block, on plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height increment) among plants grown in soil from Sites 1, 2, and 3. ............................................................................................................................................. 92 Table 3.9 Analysis of variance (ANOVA) results for the effects of the main factor amendment (compost, bare, or mulch), and random factors site and block on abundance of Pratylenchus spp. in soil and roots, total nematodes in soil, multiplication rate of Pratylenchus spp., and the percent  xii necrotic root surface area at harvest of plants grown in soil from Sites 1, 2, and, 3. ................... 93 Table 3.10 Effect of soil amendment (bare, compost, or mulch) and/ or sterilization regime (sterilized or non-sterilized) on measures of plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height increment) for plants grown in soil from Site 1.  ................................................................................................................................... 95 Table 3.11 Effect of soil amendment (bare, compost, or mulch) on population densities of Pratylenchus spp. in soil at time of planting, and Pratylenchus spp. in soil and roots, total nematodes in soil, multiplication rate of Pratylenchus spp., and the percent necrotic root surface area at harvest at Site 1. ................................................................................................................ 96 Table 3.12 Effect of soil amendment (compost, bare, or mulch) and/ or sterilization regime (sterilized or non-sterilization) on measures of plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height in increment) for plants grown in soil from Site 2. .................................................................................................................................... 99 Table 3.13 Effect of soil amendment (bare, compost, or mulch) on population densities of Pratylenchus spp. in soil at time of planting, and Pratylenchus spp. in soil and roots, total nematodes in soil, multiplication rate of Pratylenchus spp., and the percent necrotic root surface area at harvest at Site 2. .............................................................................................................. 100 Table 3.14 Effect of soil amendment (bare, compost, or mulch) and/ or sterilization regime (sterilized or non-sterilized) on measures of plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height ......................................................... 103 Table 3.15 Effect of soil amendment (bare, compost, or mulch) on population densities of Pratylenchus spp. in soil at time of planting, and Pratylenchus spp. in soil and roots, total nematodes in soil, multiplication rate of Pratylenchus spp., and the percent necrotic root surface area at harvest at Site 3. .............................................................................................................. 104 Table 3.16 Main effects, and one-way and two-way interactions of the effect of soil treatment (fumigation, compost, or legacy effects) on cation exchange capacity (CEC), electrical conductivity (EC), and nutrients phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) at Site 4 in October 2015 and 2016 sampling years. ....................................... 107 Table 3.17 Main effects, and one-way and two-way interactions of the effect of soil treatment (bare, compost, or mulch) on permanganate oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N)  xiii ratio and pH at Site 4 in October 2015 and 2016 sampling years. .............................................. 109 Table 3.18 Main effects, and one-way and two-way interactions of the effect of soil treatment (bare, compost, or mulch) on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S copy number) and total fungi (18S copy number), fungal-to-bacterial (F: B) ratio, % root colonization by arbuscular mycorrhizal fungi (AMF), and Pratylenchus spp. per one-gram root and 50-grams soil at Site 4 in October 2015 and 2016 sampling years. ..................................... 113 Table 3.19 Effect of soil treatment (fumigation effects, compost effects, or legacy effects) and/ or sterilization regime (sterilized or non-sterilized) on measures of plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height increment) for plants grown in soil from Site 4. ................................................................................................. 117 Table 3.20 Effect of soil treatment (fumigation effects, compost effects, or legacy effects) on abundance of Pratylenchus spp. in soil at time of planting, and Pratylenchus spp. in soil and roots, total nematodes in soil, multiplication rate of Pratylenchus spp., and the percent necrotic root surface area at harvest at Site 4. .......................................................................................... 118 Table A.2.1 Shoot height increment of plants grown in non-sterile and sterile soil for each site...................................................................................................................................................... 152 Table A.2.2 The eigenvalues were used to decide the number of axes to represent and display in the plot on the basis of the amount of total variance explained. The largest amount of the variance was explained by the first two principal components. ................................................. 152 Table B.3.1 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with the factors soil amendment (bare, compost, andor mulch) and site (Sites 1, 2, and, 3) on cation exchange capacity (CEC), electrical conductivity (EC), and elements phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na). ........................................... 155 Table B.3.2 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and site (Sites 1, 2, and, 3) on permanganate oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio, and pH. ........................... 156 Table B.3.3 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and site (Sites 1, 2, and, 3) on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S copy number) and fungi (18S copy number), fungal-to-bacterial (F: B) ratio, and % arbuscular mycorrhizal fungi (AMF)  xiv colonization. ................................................................................................................................ 156 Table B.3.4 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and site (Sites 1, 2, and, 3) on Pratylenchus spp. abundance in 1 g root and 50 g bulk soil, and total nematodes in 50 g bulk soil...................................................................................................................................................... 158 Table B.3.5 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on cation exchange capacity (CEC), electrical conductivity (EC), and elements phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) at Site 1. ............................................................................. 159 Table B.3.6 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on on permanganate oxidizable carbon (POXC), total nitrogen (TN), total carbon (TC), inorganic carbon (OC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio and pH at Site 1. .......................... 160 Table B.3.7 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S), and total fungi (18S), fungal-to-bacterial (F: B) ratio, and the % root colonization by arbuscular mycorrhizal fungi (AMF) at Site 1.  .............. 161 Table B.3.8 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on Pratylenchus spp. abundance in 1 g root and 50 g bulk soil, and total nematodes in 50 g bulk soil at Site 1. .......................... 162 Table B.3.9 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on cation exchange capacity (CEC), electrical conductivity (EC), and nutrients phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) at Site 2. ............................................................................. 162 Table B.3.10 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on permanganate oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio, and pH at Site 2. ................................................ 164 Table B.3.11 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S), and total fungi (18S), fungal-to-bacterial (F: B)  xv ratio, and the % root colonization by arbuscular mycorrhizal fungi (AMF) at Site 2. ............... 165 Table B.3.12 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on soil amendment (bare, compost, or mulch) on Pratylenchus spp. abundance in 1 g root and 50 g bulk soil, and total nematodes in 50 g bulk soil at Site 2. ......................................................................................... 166 Table B.3.13 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on cation exchange capacity (CEC), electrical conductivity (EC), and nutrients phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) at Site 3. ............................................................................. 166 Table B.3.14 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on on permanganate oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio, and pH at Site 3. ................................................ 168 Table B.3.15 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S), and total fungi (18S), fungal-to-bacterial (F: B) ratio, and the % root colonization by arbuscular mycorrhizal fungi (AMF) at Site 3. ............... 169 Table B.3.16  ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on Pratylenchus spp. abundance in 1 g root and 50 g bulk soil, and total nematodes in 50 g bulk soil at Site 3. ........ 170 Table B.3.17 Effect of soil treatment (fumigation, compost, or legacy effects) on cation exchange capacity (CEC), electrical conductivity (EC), and nutrients phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) at Site 4 in October 2015 and 2016 sampling years. ………………………………………………………………………………...170 Table B.3.18 Effect of soil treatment (bare, compost, or mulch) on permanganate oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio and pH at Site 4 in October 2015 and 2016 sampling years. ........................................................................................................................... 174 Table B.3.19 Effect of soil treatment (bare, compost, or mulch) on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S copy number) and total fungi (18S copy number), fungal-to-bacterial (F: B) ratio, % root colonization by arbuscular mycorrhizal fungi (AMF), and  xvi Pratylenchus spp. per one-gram root and 50-grams soil at Site 4 in October 2015 and 2016 sampling years. ........................................................................................................................... 179 Table B.3.20 Two-way, three-way, four-way, and five-way ANOVA interactions of soil treatment (fumigation effects, compost effects, or legacy effects) and/ or sterilization regime (sterilized or non-sterilized) on measures of plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height) for plants grown in soil from  Site 4. …………………………………………………………………………………………. 183                         xvii List of Figures  Figure 2.1 Location of each of the 18 orchard sites in the Okanagan Valley from which soil was collected from (0.1 m = 50 m). Details of each site are shown in Table 2.1. ............................... 32 Figure 2.2 Shoot height increment (cm) of plants grown in sterilized (solid box) and non-sterilized (open box) soil collected from 18 sites that were either ‘old’ (black label), ‘new’ (red label), or ‘non-cultivated’ (NC) (green) orchard types. ................................................................ 44 Figure 2.3 Vector loading plot of all separate variables in a two-dimensional principal component analysis (PCA) ordination of abiotic soil variables (C: N ratio, organic carbon (OC), total nitrogen (TN), POXC, phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), sodium (Na), electrical conductivity (EC), pH), biotic soil variables (FDA hydrolysis, Pratylenchus 50 g-1 soil and g-1 root), and topographic variables (elevation and latitude) for all 18 sampling sites (numbered 1 to 18). ............................................................................................... 53 Figure 3.1 a) Population density of the log (Pratylenchus spp. g-1 dry root) was positively associated with the square-root of % necrotic root surface area (n=6 for each treatment) (Pearson correlation; r=0.62; P=0.003) at Site 1. The equation of the line for the regression is y = 8.4 + 0.28x (df=17; F=10.2; P=0.006; R2=0.38). b) Population density of the log of Pratylenchus spp. g-1 dry root + 1 was negatively associated with total root surface area (n=6 for each treatment) (Pearson correlation; r= -0.5; P=0.017) at Site 1. The equation of the line for the regression is y = 49.9 – 9.3x (df=17; F=5.4; P=0.033; R2=0.38). ............................................................................ 97 Figure 3.2 a) The population density of log of Pratylenchus spp. g-1 dry root +1 was positively associated with square-root of % necrotic root surface area (n=6 for each treatment) (Pearson correlation; r=0.53, P=0.12) at Site 2. The equation of the line for the regression is y = 8.4 + 0.28x (df=17; F=6.2; P=0.024; R2=0.28). b) The population density of log of Pratylenchus spp. g-1 dry root +1 was negatively associated with total root surface area (n=6 for each treatment) (Pearson correlation; r= -0.47; P=0.023) at Site 2. The equation of the line for the regression is y = 34.5 – 4.0x (df=17; F=4.7; P=0.046; R2=0.22). ...................................................................... 101 Figure 3.3 a) The population density of log of (Pratylenchus spp. g-1 dry root + 1) was not associated with the square root of % necrotic root surface area (n=6 for each treatment) (Pearson correlation; r= -0.31; P=0.3) at Site 3. The equation of the line for the regression was y = 9.01 - 0.033x (df=17; F=0.28; P=0.6; R2=0.1). B) The population density of log of Pratylenchus spp. g-1 dry root + 1 was negatively associated with total root length (n=6 for each treatment) (Pearson  xviii correlation; r= -0.41; P=0.04) at Site 3. The equation of the line for the regression was y = 35.9 – 3.5x (df=17; F=3.2; P=0.09; R2=0.2). ......................................................................................... 105 Figure A.2.1 Standard curve for the fluorescein diacetate (FDA) hydrolysis assay. ................. 151                   xix Abbreviations AMF  arbuscular mycorrhizal fungi  ANOVA analysis of variance Ca  calcium CEC  cation exchange capacity d  day DNA  deoxyribonucleic acid EC  electrical conductivity FDA  fluorescein diacetate hr  hour K  potassium Mg  magnesium min  minute Na  sodium OC  organic carbon P  phosphorus PCR  polymerase chain reaction PCA  principal components analyses POXC  permanganate oxidizable carbon sec  second TC  total carbon TN  total nitrogen wk  week  xx Acknowledgements           I would like to express my sincere gratitude to all of those people who have supported me through my masters and the completion of my thesis. Most of all, I would like to thank Dr. Louise Nelson, and Dr. Melanie Jones, who have been my greatest mentors. I can not express how grateful I am for their guidance and support. I would also like to thank my supervisory committee members Dr. Miranda Hart and Dr. Thomas Forge, who provided me with their expert advice and guidance on data collection and analyses.       I would like to thank Agriculture and Agri-Food Canada (AAFC) and the BC Ministry of Agriculture for funding this project through the BC Agriculture & Food Climate Action Initiative under Growing Forward 2, a federal- provincial-territorial initiative. The program was delivered by the Investment Agriculture Foundation of BC. I would also like to thank the Natural Science and Engineering Research Council (NSERC) for awarding me the Canadian Graduate Scholarship-Masters (CGS-M) in 2016. I would like to acknowledge the support of all the growers who allowed me to sample soil from their orchards. Specifically, I would like to thank Niel Dendy of Dendy Orchards, and David Geen and Gayle Krahn of Coral Beach Farms Ltd., for welcoming us to do research in their orchard, and for supplying us with the necessary resources and information so we could effectively conduct our research. Also, special thanks to Shawn Kuchta and Paul Randall of AACF for their technical and methodological support.  Lastly, I am grateful to have had the opportunity to work with members of the Jones and Nelson labs. Special thanks to Tirhas Gebretsadikan, Tanja Voegel, Tristan Watson, Anton Hsu, Emilie Tremblay, Janine Siopongco, Rhiannon Wallace, and Geet Hans for their support.      xxi Dedication            I would like to dedicate this thesis to my mentors, family, and friends for their ongoing encouragement and support. It has been a difficult road, and you all helped guide me in your own way.                             1 1.0 Chapter 1: Introduction 1.1 Adapting crop production to climate change      Climate change has become a reality, making environmentally sound and sustainable crop production necessary to provide sufficient food for the increasing human population, which is expected to reach 9 billion by 2050 (FAO 2002). The altered amount and distribution of precipitation, heat, and atmospheric CO2 concentrations are all expected to alter the suitability of specific locations for crops (Neilsen et al. 2013), and agricultural productivity patterns worldwide (Brouder and Volenec 2008). It is difficult to predict whether these factors will decrease or increase the current levels of agricultural production (Schimel 2006), as both the geographic range of crops, pests and diseases, and the frequency of extreme weather events are expected to change (Neilson et al. 2013). In addition, heavy use of some fertilizers is exacerbating the effects of climate change by increasing greenhouse gas emissions, and creating serious environmental and human health problems (Gunnell et al. 2007; Leach and Mumford 2008). Therefore, crop management practices that conserve water, reduce soil erosion, and increase carbon (C) sequestration to enhance soil health, are necessary to help mitigate and adapt to climate change and contribute to the future food requirements for the world’s growing population (Lal et al. 2011).             One adaptation to climate change in the British Columbia Interior will be to expand sweet cherry (Prunus avium L.) production into northern and higher elevation areas that now have warmer temperatures and a longer growing season (Quamme and Neilsen 2011). The soil in the new areas has never been cultivated, and there is evidence to suggest that the soil microbial community in non-cultivated soils may be suppressive to soil-borne plant pathogens, and, in turn, beneficial to plant growth (Mazzola 1999). On the contrary, soil biological factors may influence   2 cherry range expansion negatively, as the replacement of a native plant with a foreign species may change the selective pressures acting on the soil microbiome (Bakker et al. 2012; Brown and Vellend 2014). For instance, the foreign plant species may secrete exudates into the soil that serve as inefficient substrates for the native microbial community, causing this un-adapted microbiome to be less effective at preventing pathogen establishment, and thereby, increasing plant susceptibility to disease (Bakker et al. 2012; Brown and Vellend 2014). Ultimately, it will be important to implement proper land use practices upon new orchard establishment in order to suitably manage plant soil feedbacks (Van der Putten et al. 2016). This could be accomplished by the use of organic amendments, cover crops, or reduced tillage, as such practices have been shown to foster microbiome characteristics that constrain disease, and increase soil health (Larkin et al. 2015).   1.2 Defining soil quality and soil health        Soil health has been defined as ‘the continued capacity of soil to function as a vital living system, within ecosystem and land-use boundaries, to sustain biological productivity, maintain the quality of air and water environments, and promote plant, animal and human health’ (Doran et al. 1996). The term soil quality has been defined by Karlen et al. (1997) as ‘the capacity of a specific kind of soil to function, within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality and support human health and habitation’. Soil health more clearly portrays the idea that soil is a living dynamic resource, while the term soil quality focuses on quantitative soil properties and the link between those properties and various soil functions (Romig et al. 1995). In this review, the term soil health will be used preferentially because it directly mentions plant health and, in agricultural   3 soils, plant productivity is the main priority.  1.2.1 Assessing soil health           Soil health is usually measured with a suite of soil biological, chemical and physical properties and processes that have the greatest sensitivity to changes in soil function (Andrews et al. 2004; Gil-Sotres et al. 2005). Although the biological, chemical and physical properties of soil will be discussed in isolation for ease of explanation, it is important to remember that these factors are all intimately interrelated. For instance, soils can vary in pH, structure, texture, organic matter content, micro-aggregate stability and the availability of nutrients. In turn, these physicochemical properties can directly select for specific microbes by creating environments that benefit certain types of microbes and influence the availability of plant root exudates affecting microbial recruitment by the plant (Lareen et al. 2016). Thus, by assessing the relationship among the major measurable biological, chemical, and physical indicators of soil health, soil fertility can be evaluated among land management and land use practices (Doran and Zeiss 2000; Doran 2002; Dalal et al. 2003).    1.2.2 Biological properties of soil health assessment 1.2.2.1 Soil organic matter    It is thought that soil organic matter (SOM) is the single most important soil health indicator because of the numerous chemical, physical, and biological properties and processes it influences (Romig et al. 1995). Soil organic matter comprises an extensive range of living and non-living components, consisting of plant and animal residues at various stages of decomposition, cells and tissues of soil organisms, and substances synthesized by soil   4 organisms (Haynes et al. 2005). The main indicators for evaluating SOM status include (1) soil organic carbon (SOC), as it comprises about 50% of SOM; (2) organic nitrogen (N), as it is associated with organic C and is an important nutrient for plant productivity; and (3) readily mineralizable C and N (Gregorich et al. 1994; Haynes 2008). Although total SOM is well-recognized as an important indicator of overall soil quality (Gregorich et al. 1994; Wander and Drinkwater 2000; Haynes 2005), it is not an ideal indicator of nutrient availability because much of the bulk SOM is in forms that turn over slowly (Drinkwater et al. 1998; Robertson et al. 2000). Thus, testing total SOM in combination with the organic and/ or labile organic matter pool can provide better insight into how changes in management affect nutrient cycling and potential soil C accumulation or loss (Haynes 2005; Lewis et al. 2011).  Mineralizable C, as well as permanganate-oxidizable C (POXC), are currently being included in soil health assessments as affordable and promising tests for active organic matter (Moebius-Clune et al. 2016). The measurement of POXC is based on chemical oxidation of organic matter by potassium permanganate solution (Weil et al. 2003). In a comparison of POXC with other more established measures of active organic matter, Culman et al. (2012) found that POXC was closely related to smaller and heavier particulate organic C fractions, indicating that POXC reflects a stabilized pool of active soil C. Their work also showed that POXC is more sensitive to changes in management than other soil C fractions, which suggests that POXC can be a useful metric for tracking management-induced changes in SOM. Mineralizable C, in comparison, correlates well with practices that promote SOM mineralization, and therefore can be a useful indicator of short-term soil nutrient availability (Hurisso et al. 2016).        Changes in SOM, particularly in biologically-available forms, are strongly linked to changes in the size (i.e. microbial biomass), activity (i.e. enzyme activity), and diversity (i.e.   5 genetic and functional structure) of the soil microbial community (Haynes 2008). These biological indicators also form an integral component in soil health assessment (Ritz et al. 2009). Furthermore, since microorganisms are involved in many soil processes, they may also give an integrated measure of soil health that cannot be obtained with physical and chemical measures alone (Nielsen et al. 2002; Mueller et al. 2010).  1.2.2.2 Microbial biomass  Microbial biomass, the living component of SOM, is considered the most labile C pool in soils and a sensitive indicator of changes in soil processes (Haynes 2008). It is generally accepted that a soil is less susceptible to plant disease-causing organisms when there is a greater soil microbial biomass (Hoitink and Boehm 1999). Theoretically, a large biomass creates an antagonistic environment that is deleterious for pathogens. One of the main goals of adding soil organic amendments is to increase microbial biomass, as organic amendments are rich in labile carbon fractions, which are a source of energy for microorganisms (Hoitink and Boehm 1999).   There is an array of techniques to measure fungal and bacterial abundance, among which are the chloroform fumigation-extraction method (Vance et al. 1987), the substrate-induced respiration method (Anderson and Domsch 1978), and phospholipid fatty acid (PLFA) analyses. Phospholipids that are specific to bacterial and fungal cellular membranes can be used to estimate the biomass of each group in soil (Frostegård and Bååth 1996), and can also be a useful tool to describe microbial diversity and structure (Bossio et al. 1998; Ibekwe and Kennedy 1998; Zelles 1999). However, an important consideration when using this technique is that these biomarkers can be quite variable within their target group (Joergensen and Wichern 2008; Weete and Gandhi 1999). Another option for measuring microbial biomass is to quantify the 16S and   6 18S gene regions, as a measure of bacterial and fungal abundance, respectively, using quantitative PCR (qPCR). The main concern about this approach is that fungal cells may include many or no nuclei, leading to an over or underestimation of fungal abundance on a per gram soil basis (Strickland and Rousk 2010). Another important consideration is that DNA is present in both active and inactive cells, and therefore, RNA or protein-based approaches may be more useful for evaluating environmental changes driven by an active microbial community (Nocker and Camper 2009).   1.2.2.3 Microbial activity         Soil microbial activity quantifies aspects of soil functioning, including biogeochemical cycles, and organic matter decomposition (Haynes 2008). Since microbial enzyme activity is the driving force behind these activities, several assays have been developed to assess enzyme activity (Burns 1982; Schnurer and Rosswall 1982). Soil enzyme activities are useful indicators of soil health because they are (1) sensitive to disturbance, (2) easily measured, (3) linked to the cycling of soil nutrients, (4) provide information on both the microbial and the physicochemical status of the soil, and (5) show response to changes in soil management (Aon et al. 2001; García -Ruiz et al. 2009). Factors that affect the activity of hydrolytic enzymes, such as dehydrogenases, appear to be related to the level of organic matter in the soil, and therefore, enzyme activities in degraded soils usually respond positively to organic matter inputs (García 1994; Ros 2003; Tejada 2006). In contrast, the response of these enzymes in soils with a naturally high organic matter content can differ, and is dependent on soil type, and the type and application rate of the organic amendment (Goyal et al. 1999; Albiach et al. 2000). Nannipieri et al. (2003) noted that the simultaneous measurement of several enzyme activities may better describe soil microbial   7 activity. Therefore, the fluorescein diacetate (FDA) hydrolysis assay, which measures the activity of more than one enzyme, is a very useful assay in soil health assessment (Green et al. 2006). FDA hydrolysis measures the activities of esterases, proteases and lyases, all of which are present in both bacteria and fungi (Green et al. 2006). Consequently, this method gives a good estimate of the activities resulting from the whole microbial community responsible for FDA hydrolysis.  1.2.2.4 Microbial diversity           Soil genetic and functional biodiversity are major areas of interest among soil scientists. Reductions in the abundance and presence of various groups of soil biota (e.g. fungi, bacteria, and nematodes) result in a decrease in multiple ecosystem functions, including plant diversity (Wagg et al. 2014), decomposition (Gessner et al. 2010; Wagg et al. 2014), nutrient retention and availability (Wagg et al. 2014), and the susceptibility of soil to invasion by pathogens (Van Elsas et al. 2012). When there is more than one species present to carry out a particular ecological function, this is called ‘functional redundancy’ (Beare et al. 1995). This means loss of many species, or perhaps even entire functional groups of soil organisms, can be compensated for in some soils by the activities of other organisms (Wardle 2002). Theoretically, taxonomic or genetic diversity, which is associated with greater functional diversity, should lead to more consistent functioning of the soil microbiome (Loreau et al. 2001).  1.2.3 Soil physical and chemical properties of soil health assessment     Key soil physical properties, such as soil structure, soil architecture, water infiltration rate, bulk density, and rooting depth, form the foundation of crucial chemical and biological   8 processes. Sand, silt and clay particles are the basic building blocks of soil and comprise its mineral fraction. The way these particles are arranged into larger units or aggregates determines its structure, and the nature of the pore spaces between soil particles. Soil physical architecture (e.g. porosity, pore size distribution, pore connectivity) controls oxygen diffusion rate, water flow and nutrient supply for microbial communities and vascular plants (Dungait et al. 2012). Lately, the concept of physical protection governing the fate of soil C has gained wide recognition (Chenu and Planta 2006; Dungait et al. 2012; Schmidt et al. 2011). It is a process in which spatial disconnection causes soil C to be inaccessible to microbial decomposers and their enzymes (Dungait et al. 2012). Therefore, pore aggregate size and geometry can affect how long aggregates store organic carbon, depending on microbial accessibility, and climatic and edaphic factors (Six et al. 2002). This is an important consideration when subjecting soil to a disturbance or land use change. Although the mineral fraction is the major component of soil, the organic fraction (which usually makes up only 1–4% of the weight of most agricultural soils) is the key factor driving the majority of soil functions. SOM plays a critical role in creating and stabilizing soil structure, which in turn produces good tilth, adequate drainage and the capacity to resist erosion; it increases soil water availability by enhancing water infiltration and soil water-holding capacity; and it reduces bulk density, which is associated with improved aeration, and, ultimately, results in greater root growth (Dalal and Moloney 2000).   Many important soil health indicators are chemical in nature, and they greatly affect a range of physical and biological properties, again, illustrating the relationship among these factors. Soil pH is mainly a function of soil parent material, time of weathering, vegetation and climate, and is considered as one of the dominant chemical indicators of soil health, as it affects soil salinisation, crop performance, nutrient availability and cycling, and biological activity   9 (Dalal and Moloney 2000). Most agricultural soils fall within a pH range of 5.5 to 7.5, however, practices such as the application of ammonium-based fertilizers, and the leaching of excess nitrate, and accompanying cations, such as calcium (Ca), are all acidifying processes (Dalal and Moloney 2000). Since humic substances have a capacity to resist changes in soil pH, management practices that increase soil organic matter content can lead to increased soil buffering capacity (Dalal and Moloney 2000). Another important property of soil organic matter is that it increases the sorption capacity and cation exchange capacity (CEC) of soil. These properties determine the retention of major nutrient cations, such as Ca, magnesium (Mg) and potassium (K), and the immobilisation of potentially toxic cations, such as aluminum (Al), as well as pesticides and chemicals (Dalal and Moloney 2000; Ross et al. 2008).    Since soil organic matter is derived primarily from plants, it serves as an important source of all the macronutrients (N, Ca, K, Mg, phosphorus (P), and sulphur (S)) and micronutrients (Mn, boron (B), chlorine (Cl), copper (Cu), iron (Fe), molybdenum (Mo), zinc (Zn) and selenium (Si)) required by crops (Dalal and Moloney 2000). Furthermore, soil humus contains much of the N, P, and S found in soils, thus providing the principal long-term storage medium and the primary short-term source of these nutrients (Weil and Magdoff 2004). Numerous studies have shown that even when inorganic N fertilizer is applied, most of the N taken up by a crop comes from mineralization of soil organic pools rather than directly from the current year’s fertilizer (Omay et al. 1998).   1.3 Role of arbuscular mycorrhizal fungi in soil and plant health     Arbuscular mycorrhizal fungi (AMF) are ubiquitous soil microbes that form symbiotic associations with roots in 80% of plant species studied (Harrier and Watson 2004; Wang and Qiu   10 2006). Many agricultural crops form mycorrhizal associations, and there is much evidence that suggests crop plants can benefit from the mutualistic relationship (Augé 2004; Kohler et al. 2009). Depending on the host plant and the isolate of AMF, colonization of root systems can improve P, Ca, Cu, Mn, and Zn nutrition of the plant (Clark and Zeto 1996), due to the ability of the fungal hyphae to grow beyond the nutrient depletion zone that develops in the rhizosphere (Smith and Read 1997). In return, the plant supplies the AMF with carbon. In addition, AMF colonization has been shown to improve drought resistance and water stress tolerance of their host in some cases (Augé et al. 1994; Smith and Read 1997; Augé 2001), but not others (Bryla and Duniway 1998). Lastly, AMF root colonization has been shown to increase plant resistance to plant parasitic fungi and nematodes (Pinochet et al. 1993; Forge et al. 2001; Whipps 2004).   In the case of agricultural soils, the effect of modern day agricultural practices, such as liming, fertilization, biocide application, and tillage, on the AMF association is not clear-cut, with each having varying effects on AMF diversity and percent root colonization (Gosling et al. 2006). For example, fertilizer usage is common in areas of intensive crop production, and excessive P-fertigation may cause a buildup in available P and total P, and result in the failure of AMF to colonize crop plants (Kahiluoto et al. 2001; Sorensen et al. 2005). However, there have also been reports of P fertilization intensifying AMF colonization in soils high in available P (Vosatka 1995; Ryan and Ash, 1999). The effects of organic sources of nutrients on AMF colonization are even more unpredictable. In Meyer et al. (2015), the use of straw mulch and compost promoted colonization of apple roots with native AMF, and colonization was positively associated with P, Ca, and Mg leaf concentrations, but negatively associated with apple yield. Other studies have shown that excessive use of organic amendments, which are a source of multiple macronutrients, such as P and N, can result in a decrease in AMF root colonization   11 (e.g., Jordan et al. 2000; Cavagnaro 2014). Interestingly, Cavagnaro (2014) showed that although the formation of AMF had relatively little impact on plant growth at a high compost application rate, when the compost was applied at a lower rate, the formation of AMF had a strong positive association on plant P and Zn. These results suggest that AMF can interact with compost to influence nutrient supply to plants in agricultural soils.       When replanting young trees into orchards that have continuously cropped the same or related tree species, low P availability is related to poor replant establishment (Forge et al. 2016). Since improved P nutrition is one of the primary benefits that AMF provide to their host, a low level of AMF root colonization that does not enhance P uptake can result in poor replant establishment (Forge et al. 2001; Baum et al. 2015). However, there are numerous reasons for poor performance of replant trees and, although low AMF colonization is not the main cause, it could cause roots to have limited capacity for nutrient uptake, thereby increasing susceptibility to pathogen invasion (Forge et al. 2001).  1.4 Replant stress of fruit trees          In fruit tree orchards, the soil remains undisturbed for up to several decades, yet soil health is dramatically impaired, as indicated by the poor growth of young trees, of the same or related species, planted later on the same site. There have been many terms used to describe this effect, including replant stress, soil exhaustion, soil sickness, continuous cropping obstacle and, more commonly, replant disease (RD) (Mai and Abawi 1981; Mazzola and Manici 2012). The problem is common to all major tree fruit growing regions of the world, but may result from a combination of site-specific factors (Mai and Abawi 1981). Some researchers have postulated that there are two categories of RD: (1) specific RD, in which the same species is affected by the   12 soil (i.e. growth of apple, not cherry, would be suppressed in soil from an apple orchard), and (2) non-specific RD, in which multiple types of fruit trees are affected by growth in the affected soil (Mai and Abawi 1981). However, much controversy exists over the specificity of RD due to reports of orchard soil showing characteristics of both specific and non-specific RD (Mai and Abawi 1978; Sewell 1981).            Replant disease is usually expressed as stunted growth, low productivity and a decline in tree vigor leading to shortened economic life (Dullahide et al. 1994; Mazzola 1998; Manici et al. 2003; van Schoor et al. 2009). Within 1-3 months of replanting, newly planted trees impacted by RD will show symptoms such as reduced shoot growth, root tip necrosis, decreased root biomass, and unequal growth amongst the newly planted trees (Mazzola and Manici 2012). In the long-term, the effects of RD can cause a delay in initial fruit production, and decreased fruit quality and yield (Tewoldemedhin et al. 2011). Therefore, RD can prevent a young orchard from attaining a level of productivity comparable to an unaffected orchard (Mazzola 1998).  1.4.1 Causes of replant stress  The causal agents and factors implicated in replant problems vary considerably among both geographic regions and orchards in the same region (Mazzola 1998). This variability has made it difficult to determine a universal specific etiology of RD (Tewoldemedhin et al. 2011), but numerous soil-borne organisms have been implicated. Root-lesion nematodes, such as Pratylenchus penetrans, are reported to have a major role in RD in some regions (Mai and Abawi 1981; Utkhede et al. 1992; Dullahide et al. 1994), as they attack the roots of trees of every size and age (Mai and Abawi 1981). Other studies have reported parasitic fungi to be primary causal agents, particularly fungi from the genera Rhizoctonia, Fusarium, Pythium, Phytophthora,   13 and Cylindrocarpon-like fungi (Ilyonectria spp. and Thelonectria sp.) (Sewell 1981; Utkhede and Smith 1991; Dullahide et al. 1994; Gu and Mazzola 2003; Manici et al. 2003). Replant disease in Prunus spp. has also been associated with an increase in rhizosphere bacilli and higher populations of cyanogenic microorganisms (Benizri et al. 2005).      Abiotic factors, such as inadequate P availability, low or high soil pH, phytotoxins, heavy metal contamination, poor soil structure or drainage, and cold or drought stress, have also been associated with replant disease (Mazzola 1998; Slykuis and Li 1985). Nevertheless, the fact that soil pasteurization (Jaffee et al. 1982; Yim et al. 2013) or fumigation (Mai and Abawi 1981; Slykhuis and Li 1985) improves plant growth in soils from RD-affected orchards provides evidence that the disease is due more to biotic than abiotic factors (Mazzola, and Mullinix 2005). In turn, if abiotic factors are not optimal, they can exacerbate the effects of soil organisms associated with RD (Mai and Abawi 1981; Utkhede et al. 1992). Therefore, it is critical that replant mitigation and management strategies account for both biotic and abiotic components of the replant complex.  1.4.1.1 Plant-parasitic nematodes          Most nematodes in soil are free living, but some species infect plant roots, and cause more than a 20% yield loss for some economically important crops (Bird and Kaloshian 2003). The group of nematode species that infect roots are collectively referred to as plant-parasitic nematodes. All plant-parasitic nematodes have a stylet, which is a strong, hollow, needle-like structure that is used to pierce plant cells, inject nematode secretions, and, subsequently, to feed on plant cell contents (Bonkowski et al. 2009). Stylets vary in shape and size depending on the feeding strategy of the nematode (Ravichandra 2014). Classifications include ectoparasites,   14 which do not enter root tissues, but use their long stylets to feed on root cells; migratory endoparasites, which penetrate and move into the root interior; and sedentary endoparasites, which develop a feeding site in the root where they reproduce (Hussey and Grundler 1998; Dropkin 1969). Most damage to crops is caused by a relatively small number of the dozens of genera that infect crop plants (Ravichandra 2014), such as the sedentary endoparasites Meloidogyne (root-knot nematode) and Heterodera (cyst nematode); migratory endoparasites, such as Pratylenchus spp. (root-lesion nematode); and a more limited number of ectoparasites (e.g. Xiphenema spp. or dagger nematode) (Bird and Kaloshian 2003). In the Okanagan Valley of British Columbia, Canada, Pratylenchus penetrans is widely distributed (Vrain and Yorston 1987), and has been implicated in poor replant growth of apple (Malus domestica, Borkh.) (Utkhede and Smith, 1991). Forge et al. (2013) conducted a more recent survey of unhealthy sweet cherry (P. avium L.) orchards in the region, and P. penetrans was found in all sites surveyed.            The nature of the host-parasitic interaction is thought to vary among parasitic nematodes. In the case of Pratylenchus spp, they rely on chemical gradients to be attracted to the root, migrate towards the roots, and locate a feeding site (Gheysen and Jones 2006). At the root surface, they undertake probing and sensory perception before selection of a cell from which to feed (Bonkowski et al. 2009). When an appropriate plant cell is located, cell perforation by the stylet, salvation, and food ingestion occur (Zunke 1990). Although mobile juvenile and adult stages can enter and leave the root, stage two and three juveniles tend to feed from root hairs, and later stages move into the root to feed endoparasitically (Gheysen and Jones 2006). Once they penetrate plant cell walls, they can migrate from cell-to-cell, and suppress or evade host defenses by means of a suite of cell wall modifying enzymes (Jones and Fosu-Nyarko 2014).   15 Comparisons of host plant transcription patterns have shown that parasitic nematode infection can initiate complex changes in plant gene expression, as plant genes involved in defense responses are up-regulated upon infection (Gheysen and Jones 2006). Thus, plants are not passive to nematode parasitism, and exert many defense strategies in order to ward off nematodes once they have been detected within the root (Gheysen and Jones 2006). The identification of such nematode resistance genes is important in the development of nematode-resistant plant cultivars (Atkinson et al. 1998). However, resistance genes are scarce and the trait(s) can be difficult to transmit to the next generation (Ledbetter 1994). Therefore, difficulties in breeding for nematode resistance, coupled with the decreasing availability of nematicidal chemicals, suggests there is a need to study and develop sustainable soil management strategies for managing plant-parasitic nematodes.  1.5 Strategies for managing soil-borne pests and pathogens      Management practices, such as organic amendment application, have been used for centuries for the purpose of crop nutrition and to improve soil fertility, but in many temperate cropping areas they have not been used extensively since the advent of synthetic agri-chemicals (van Bruggen 1995). Modern agriculture relies on fumigants, fertilizers and pesticides to increase food production, but at the expense of ecosystem health. For instance, broad-spectrum soil fumigants, such as methyl bromide, have traditionally been the primary management strategy used to control pathogens associated with replant stress of tree fruits, but their use is becoming more restricted due to growing concerns about the impacts of chemical fumigants on environmental and human health (Duniway 2002). Specifically, contamination from nutrients and toxic chemical compounds occurring in groundwater and surface waters, can result in   16 eutrophication and soil quality degradation (Tilman et al. 2002). In addition, fumigants and fungicides are toxic and will kill beneficial microbes, allowing re-establishment of pathogens at fumigated sites (Utkhede and Smith 1993). The heavy usage of foliar fungicides in perennial systems has non-target effects on beneficial soil bacteria and fungi (i.e. arbuscular mycorrhizal fungi) due to residue accumulation at the soil surface (Graham et al. 1986; Mackie et al. 2012). In addition, the herbicide glyphosphate can be toxic to soil microbes (Steinrucken and Amrhein 1980), and it has even been shown to affect woody perennial bark at as low as 1%, thus weakening the plant’s physical barrier to attack by pathogens (Levésque and Rahe 1992). Therefore, a strong pressure to reduce reliance on agri-chemicals is creating a renewed interest in sustainable management practices, such as the use of organic amendments, crop rotations, cover crops, green manures, conservation tillage, and a greater adoption of indicators and analyses, including biological, physical and chemical attributes, that provide a more complete picture of soil health and function (Litterick et al. 2004). Many of these practices also generally have positive effects on the management of soil-borne diseases through a number of mechanisms, including increasing soil microbial biomass, activity, and diversity, resulting in greater biological suppression of pathogens and diseases (Larkin 2015). Nevertheless, organic management practices do not always result in plant pathogen and disease suppression, and there are many inconsistencies in efficacy associated with such practices when managing soil health and reducing plant diseases (Larkin 2015).  1.6 Soil Supressiveness           All soils provide some degree of biological buffering against most soil-borne pests and pathogens, and this is referred to as ‘general’ or ‘non-specific’ soil suppressiveness (Huber and   17 Watson 1970; Hornby 1983). This type of suppressiveness can be deduced from the disease severity following pathogen inoculation in sterilized soils compared with non-sterilized soils and is attributed to the activities of the total soil microbial community in the rhizosphere and bulk soil that inhibit the growth or activity of a pathogen at some stage in its life cycle (Westphal 2005; Mendes et al. 2011). In agricultural systems, soil management practices such as soil fumigation may reduce natural soil suppressiveness, turning a suppressive soil into a non-suppressive one (Weller et al. 2002). By contrast, organic amendments can further stimulate the activity of indigenous microbial populations in the rhizosphere, resulting in enhanced general disease suppressiveness (Westphal and Becker 1999), although an influence of the amendments on abiotic factors may also play a role (Noble and Coventry 2005; Berendsen et al. 2012). For instance, suppression of P. penetrans on red raspberry and apple roots can be achieved by application of paper mulch (Forge et al. 2003; Forge and Kempler 2009). The mechanism(s) by which soil microorganisms reduce the activity of plant pathogens is largely a function of the microbial communities present in the organic amendment, as well as the communities stimulated by addition of the amendment (Mehta et al. 2014). The mechanisms associated with organic matter-mediated suppression include: antibiosis (Fravel 1988; Bakker et al. 2012; Mendes et al. 2011), competition for trace elements, nutrients, and microsites (Chen et al. 1988; Serra-Wittling et al. 1996), hyperparasitism (Heydari and Pessarakli 2010), and induced systemic resistance (Yang et al. 2009).   A second form of suppression known as ‘specific’ suppressiveness differs from general suppressiveness, as its effects are attributed to specific groups of antagonistic organisms that have well understood modes of action against pathogens (Weller et al. 2002). For instance, increased abundance of bacteria that produce the inhibitory compounds 2,4-  18 diacetylphloroglucinol (DAPG) and pyrolnitrin (PRN) coincided with increased suppression of Pratylenchus penetrans in replanted sweet cherry orchard soil amended with compost (Watson et al. 2017). Furthermore, the production of DAPG by some fluorescent pseudomonads was increased in soil that cropped wheat prior to planting apple, and resulted in the suppression of Pythium and Rhizoctonia spp. (Gu and Mazzola 2000). Due to the sheer complexity of field soils, it is clear that more detailed studies will be required to characterize biologically-based soil suppressiveness.   1.7 Organic soil amendments          Intensive agriculture, without restoration of soil organic C negatively affects soil chemical properties by causing a reduction in soil C content, which, in turn, produces deleterious effects on soil microbial biomass, soil enzymatic activities, and functional and species diversity (Bonanomi et al. 2011). A large body of research carried out in different agricultural systems has demonstrated that the addition of organic amendments can be an effective tool to recover soil organic C (Hargreaves et al. 2008; Zhang et al. 2015). The addition of chemical fertilizers also generally leads to a rapid mineral N release, while organic amendments cause a slow mineral N release, that is extended over time (Claassen and Carey 2006). Weber et al. (2007) reported that the slow mineralization of N in soils under compost amendment improves not only the soil fertility, but also the conditions of organic matter mineralization.      Organic soil amendments can be grouped into five categories: animal manure, municipal bio-solids, green manure and cover crops, waste from manufacturing processes (i.e. paper or wood mulch), and compost (Goss et al. 2013). There is increasing evidence of the impact of these materials on soil health; however, many inherent characteristics of the different types of   19 organic amendments are associated with widely different effects on the balance between benefical soil microorganisms and plant pests (Abawi and Widmer 2000; Albiach et al. 2000; van Bruggen and Semenov 2000; van Bruggen and Termorshuizen 2003; Litterick et al. 2004). Other important properties of applied residues include CEC, electrical conductivity (EC), cellulose and lignin content, carbon: nitrogen (C: N) ratio, N content, pH, the presence of toxic compounds, hydrophobicity, particle size, its preparation method (i.e. composted versus non-composted material), and application rate and method (Goss et al. 2013).   1.7.1 Factors influencing the effects of organic soil amendments     Cation exchange capacity (CEC) is strongly related to soil organic matter content, so the application of organic amendments increases soil CEC (Ross et al. 2008). High values of CEC make essential nutrient cations available for crop production (Bulluck et al. 2002). Nevertheless, one of the most worrying aspects of the use of organic amendments is the increase in EC of soils (Schulz and Glaser 2012; Bonanomi et al. 2014). High EC, and associated increases in salinity and sodicity, have negative effects on soil structure, crop yields (Maas and Hoffman 1977), and soil biological activities (Rietz and Haynes 2003).         The C: N ratio of the organic amendment incorporated into soil is considered to be an important property to predict organic C mineralization rate, and nutrient release (Parton et al. 2007). Saprophytic microorganisms that decompose organic resides in soil require both organic C and N in a relatively fixed stoichiometric ratio, so the quality of the amendment affects their growth (Fannin et al. 2015). Organic N can limit microbial growth when the C: N ratio is above the value of ~25-30, decreasing microbial feeding rate and organic matter decomposition rate, and resulting in long-term C storage (Zhang et al. 2015). When high C: N ratio amendments are   20 incorporated into soil, mineral N can be immobilized within microbial biomass, and thus decrease plant growth and yields (Hodge et al. 2000). Since a complete N immobilization is not acceptable under intensive farming systems, chemical N fertilizer would need to be applied in addition to the high C: N amendment to meet crop needs. In general, high C: N amendments favor the growth of oligotrophs (slow growth rate), such as fungal decomposers (Holland and Coleman 1987), and Acidobacteria (Bastian et al. 2009). By contrast, low C: N amendments (<20) favor the growth of copiotrophs (fast growth rate), such as pseudomonads (Bastian et al. 2009). Although low C: N ratio amendments provide only a small contribution to soil organic C, they are of higher biochemical quality, and can stimulate the microbial mineralization of more stable and recalcitrant soil organic C fractions through the priming effect (Fontaine et al. 2007). Therefore, sustainable management of soil health requires the identification of organic amendments with biochemical characteristics that effectively balance the trade-off between organic C maintenance and recovery, and nutrient mineralization (Goss et al. 2013).   The enhancement of soil suppressiveness by both composted and non-composted amendments has been demonstrated (Aryantha et al. 2000); however, several studies have stated that composted materials are more suppressive to multiple soil-borne diseases than non-composted ones (Hoitink and Boehm 1999; Noble and Coventry 2005). The effectiveness of composts appears to be related to their lower free nutrient content relative to non-composted material (Hoitink and Boehm 1999). The composting process entails three temperature phases, each of which is carried out by a different microbial community (Mehta et al. 2014). The first phase is a mesophilic phase (up to 40 °C), the second is a thermophilic phase (over 40 °C), and, finally, there is a mesophilic maturation phase (up to 40 °C) (Mehta et al. 2014). If composts are adequately stabilized in the final stage, then biological activity in the material will have slowed,   21 allowing the temperature to drop and for subsequent re-colonization of mesophilic organisms (Mehta et al. 2014). These organisms may have biocontrol activity, leading to natural disease suppression when the material is applied to soil (Hoitink and Boehm 1991).  Whether amendments are surface applied or incorporated by tillage can influence the decomposer communities and pathogens present (Holland and Coleman 1987; Govaerts et al. 2007). Incorporated amendments are in intimate contact with soil organisms and are readily decomposed, while surface-applied amendments can cause slower, more variables rates of decomposition, resulting in stratification of nutrients through the soil profile (Govaerts et al. 2007). Due to the heterogeneity of resources, surface amendments can sometimes increase microbial diversity, which may, in turn, increase the ability of soils to suppress disease (Stirling et al. 2012). However, this is a slow process that is initially constrained to the top few centimeters of soil (Yang et al. 2003). Over the long term, after repeated applications of amendments, the soil depth that is affected by mulching may increase, thus increasing soil organic matter levels and leading to changes in microbial biomass, community structure, and diversity (Bandick and Dick 1999; Peascock et al. 2001), and subsequently an increase in soil suppressiveness (Albiach et al. 2000; Bonilla et al. 2012; Pérez-Piqueres et al. 2012).  1.7.2 Indicators of disease-suppressive organic amendments     There are many instances of soil-borne pathogens being controlled effectively by the application of organic amendments; however, disease incidence can sometimes increase after the addition of amendments (Tilston et al. 2002). The effect of organic soil amendments on plant diseases depends on the plant pathosystem (Lumsden et al. 1983), the rate of application (Boulter et al. 2002), the nature/type of amendment (Pankhurst et al. 2002) and the degree of maturity of   22 composts or the decomposition stage of the crop residues (Erhart et al. 1999). A recent meta-analysis on the application of organic amendments as a strategy for the management of diseases caused by soil-borne pathogens found that an amendment suppressive to one pathogen could be ineffective, or even conducive, to growth of other pathogens (Bonanomi et al. 2010). The study also considered which physical, chemical and biological parameters most accurately identify suppressive organic amendments. Overall, enzymatic and microbiological parameters were much more informative for predicting suppressiveness than chemical ones. Fluorescein diacetate (FDA) hydrolysis activity, substrate respiration, microbial biomass, total culturable bacteria, fluorescent pseudomonads and Trichoderma populations were among the most useful indicators. The integration of several parameters may be the most promising approach for identification of suppressive soil amendments (Bonanomi et al. 2010). In general, the inconsistency of organic amendments has hindered the practical use of these materials, and indicates that further research is required to increase the predictability of their use.  1.8 Background to the study          In the British Columbia Interior sweet cherry is a very economically important crop, as 1416 hectares of land are used to produce 14 million kg of cherry annually (BC Ministry of Agriculture 2016). With a farm receipt value of 55 million dollars in 2015, receipts from cherry surpassed apple, making cherry BC’s most lucrative fruit industry (BC Ministry of Agriculture 2016). However, the cost of irrigation, spraying, labour, and propensity to damage and disease make cherry an expensive crop for growers, and the area of suitable land for cherry fruit production in BC is limited (Utkhede and Thomas 1988). One way farmers are trying to increase cherry yields is by planting dwarfing rootstocks, which are smaller and can be planted closer   23 together than conventional trees, in order to produce more fruit at a higher efficiency (Lang 2000). Until recently, before replanting cherry, the primary means of mitigating replant stress has been to fumigate the soil. The lack of access to fumigants in agriculture, due to environmental and human health regulations, emphasizes the importance of developing alternative methods for mitigating and controlling replant stress of cherry, such as the application of organic amendments, which have been shown to improve early growth of cherry (Watson et al. 2017).  It is predicted that the effects of climate change will increase the area suitable for cherry production into northern and higher elevation areas, which will now have warmer temperatures and a longer growing season (Neilsen et al. 2013). Therefore, rather than risk planting new trees on unhealthy, degraded soil, growers may now take the opportunity to plant trees on soils that have never before supported tree fruits. Environmental models for new cherry production have considered climate and soil physicochemical properties; however, they have not considered soil biological factors, which may also influence cherry range expansion (Brown and Vellend 2014; Neilsen et al. 2014; Pickles et al. 2015). These previously non-cultivated soils are also of interest because they have never been fumigated and, therefore, they provide a unique opportunity to study the indigenous microbial communities present in these soils at the onset of orchard establishment.            As discussed above, a number of studies have addressed the effect of orchard soil management on the growth of newly-planted trees in replant stress-prone sites (Miethling et al. 2000; Hoagland et al. 2008; St. Laurent et al. 2010). However, few studies have taken more proactive approaches to maintain soil health, and mitigate any future soil-borne disease in newly established orchard soils that were never previously planted to sweet cherry or other types of  fruit trees (Mazzola 1999). Using greenhouse experiments, my MSc research investigated how   24 biological factors of soils of both new and old orchards affected cherry growth. Using field experiments, I also examined how soil amendments affected soil biotic and abiotic properties in young cherry orchards in the north Okanagan that were planted in soils that were not previously cultivated, as well as in older, central Okanagan cherry orchards.  1.9 Objectives and Hypotheses                      My thesis forms a part of a larger collaborative study on the effects of orchard floor management and irrigation scheduling techniques on soil health and tree health in young, northern Okanagan sweet cherry orchards (Prunus avium L.), planted into newly cultivated soil and older cherry orchards planted into central Okanagan sites used for orchard production for an extended period (>10 years). The specific objectives and hypotheses of my Masters thesis research were as follows: Objective 1: To determine if the intensity of replant stress is related to the amount of time that the soil has supported cherry production. A greenhouse bioassay was done to determine the growth response of ‘Crimson’ sour cherry (Prunus ceraseus) plants to sterilization of soil collected from 18 orchards that ranged in the amount of time they had supported cherry production. In addition, using biotic and abiotic soil health indicators from each orchard, the factors that best predicted plant growth across orchard ages was assessed. My specific hypotheses were: • Plants grown in sterilized soil from older cherry orchards would exhibit greater growth relative to the non-sterile counterpart. I rationalized that there is a higher abundance and activity of soil-borne plant pathogens in soil that has previously cropped cherry or a related tree fruit, which may inhibit cherry plant growth.   25 • Plants grown in non-sterile soil from new cherry orchards would exhibit greater growth, relative to the non-sterile old orchard counterpart. I rationalized that there are likely more beneficial microbes, and fewer biological impediments to growth of cherry trees planted on sites that have not previously grown cherry, or a related tree fruit.  • Soils from new orchards would have greater microbial activity (as measured by FDA hydrolysis) and a lower population density of Pratylenchus spp. in soil and roots, relative to plants grown in soil from older orchards. I rationalized that the previously non-cultivated soil in new orchards likely has greater levels of soil organic carbon that can support an active soil food web capable of suppressing root-lesion nematodes.  Objective 2: To test how surface-applied compost and woodchip mulch affect soil biotic (e.g. soil microbial activity and abundance) and physicochemical (e.g. pH) properties compared to non-amended control plots in two young sweet cherry orchards planted on previously non-cultivated sites, and one older sweet cherry orchard in the Okanagan Valley over two growing seasons. In addition, effects of the above treatments on the population densities of Pratylenchus spp. in soil and roots were assessed at each site. My specific hypotheses were: • Since low C: N ratio composts are a source of multiple macronutrients, such as P and N, this treatment would have greater soil nutrient levels relative to non-amended soil in all orchard soils after two growing seasons. By contrast, there would be no appreciable difference between woodchip mulch-amended soil and non-amended soil because woodchip mulch does not contain significant amounts of nutrients relative to carbon (C: N ratio >100:1), and therefore would not likely release significant amounts   26 of nutrients as it decomposed. • Compost- and mulch- amended soils would have higher microbial activity (as measured by FDA hydrolysis), and microbial abundance (as measured by 18S fungal and 16S bacterial gene copy number) relative to the non- amended control after two growing seasons. I rationalized that carbon inputs from amendments provide a nutrient source capable of sustaining an active and abundant soil food web.  • The abundance of Pratylenchus spp. in soil and roots would be lower in compost- and woodchip-amended soil relative to non-amended soil. I rationalized that carbon inputs from compost and woodchips can create an active soil food web that is capable of suppressing root-lesion nematodes in soil and roots.  • Arbuscular mycorrhizal root colonization would be lower in compost-amended soil, relative to non-amended soil. By contrast, trees under woodchip mulch would have a similar AMF root colonization to non-amended soils. I rationalized that there are likely more readily available plant nutrients in compost-amended soil that roots can access without the benefit of the AMF symbiosis, while trees growing in non- and woodchip-amended soils may benefit from the carbon cost of an AMF symbiosis in order to acquire nutrients beyond the nutrient-depleted root zone.   27 Objective 3: To test how fumigation, compost, and legacy effects of woodchip mulch and P-fertigation, as well as interactions among these factors, affect soil biotic (e.g. soil microbial activity and abundance) and physicochemical (e.g. pH) properties compared to non-treated control soil at one old Okanagan Valley sweet cherry orchard over two growing seasons. In addition, the effects of the above treatments on the population densities of Pratylenchus spp. in soil and roots at this orchard were assessed. My specific hypotheses were: • Fumigated soil would have lower microbial abundance and activity, and population density of Pratylenchus in soil and roots, relative to the non-fumigated soil in the first growing season. However, in the second growing season, there would be little to no difference in population density of the root-lesion nematodes in fumigated soil, relative to non-fumigated soil. I rationalized that in the first growing season fumigation would destroy harmful and beneficial microorganisms, and therefore, in the second growing season there would be less microbial activity to buffer against pathogen colonization.  • Since the compost and woodchip mulch were incorporated into the soil and made readily accessible to saprotrophic soil microorganisms, this treatment would have higher soil nutrient levels and microbial activity, relative to non-amended soils after two growing seasons. • Soils fertigated with P would have higher P levels relative to the non-treated control soils after two growing seasons. However, trees planted in these plots would have lower root colonization by AMF, relative to the control. I rationalized that higher available P and total P in soil may result in the failure of AMF to colonize trees in this treatment, since the carbon cost to the plant may outweigh the potential benefit of a   28 symbiosis with AMF. Objective 4: Use a greenhouse bioassay to assess the main and interaction effects of soil sterilization (i.e. sterilized or non-sterilized) and soil treatment (compost, mulch, or no amendment) on cherry plant growth in soil collected from two new and two old Okanagan Valley orchards (same orchards as per objectives 2 and 3). In addition, this assay assessed the effect of soil treatment on population densities of Pratylenchus spp. in soil and roots. My specific hypotheses were: • Plants grown in mulch- or compost-amended soil would be larger than those grown in non-amended soil from the same orchard. The amended soils would have greater organic matter, which not only provides greater aeration and macro-porosity for container-grown plants, but may also stimulate an active soil microbial community capable of suppressing root-lesion nematode population densities in soil and roots of plants. • Plant growth would be lower when grown in sterilized mulch- or compost-amended soil, compared to non-sterilized soils from the same soil treatments, as the microbial communities in amended soil are more active and diverse, thereby supporting the plant in mineral nutrient uptake by means of decomposition. • Non-amended soil from older orchards would be less conducive to plant growth than non-amended soil from the younger orchards planted in previously non-cultivated soil. I rationalized that the population densities of soil pathogens in soil from older orchards is likely to be greater, and more deleterious than soil from new orchards.   29 2.0 Chapter 2: Soil biota from newly established orchards are more beneficial to early growth of cherry than biota from older orchards 2.1 Background           Growth of young fruit trees replanted into old orchard soil is often poor relative to soil that has not previously cropped any tree fruits (Mazzola 1999; Mazzola and Manici 2012). There is evidence that a consortium of plant parasitic nematodes and fungi, as well as associated abiotic factors, such as low P-nutrition or poor water availability, play a role in the development of replant stress in old orchard soils (Mazzola and Manici 2012). In the production of perennial tree fruits, although the soil environment remains physically undisturbed for up to several decades, there may be an extensive modification to soil biology, which is evidenced by the fact that soil pasteurization (Jaffee et al. 1982; Yim et al. 2013), or fumigation (Mai and Abawi 1981; Slykhuis and Li 1985) improves plant growth in soils from orchards with a long history of tree fruit production (Covey et al. 1979; Mazzola, and Mullinix 2005).     The economic effects of replant stress can be substantial. For instance, in the Okanagan Valley of British Columbia, Canada, the detrimental impact of replant problems on fruit yield can cost growers up to $10,000 hectare-1 annually (BC Ministry of Agriculture 2015). This is compounded by the growing economic demand that Okanagan growers are facing to export their cherries to foreign markets (BC Ministry of Agriculture 2015). Environmental models predict that cherry trees can be grown in more northern and higher elevation areas of the Okanagan Valley, due to warmer temperatures and a longer growing season brought about by climate change (Neilsen et al. 2013). This means that growers in the Okanagan Valley who are interested in expanding their cherry plantings can now invest in non-cultivated land, and plant cherry trees in soil that was not previously suitable to grow cherry. However, these models have only   30 considered how climate and soil physicochemical indicators will influence cherry growth in new regions, and they have not considered if soil biology will have a positive or a negative effect (Neilsen et al. 2014). Non-cultivated soils have been shown to have a greater microbial abundance, and diversity than cultivated soils (Postma-Blaauw et al. 2010, 2012), which are factors associated with a greater ability of soils to suppress disease caused by soil pathogens, and, in turn, benefit plant growth (Mazzola 1999; Bonilla et al. 2012. Conversely, soil biological factors may influence cherry range expansion negatively, as the replacement of a native plant with a foreign species may change the selective pressures acting on the soil microbiome (Bakker et al. 2012; Brown and Vellend 2014).    The first objective of this study was to evaluate the influence of soil biology on cherry growth by measuring plant growth response to sterilization of soil from 18 orchards with differing sweet cherry cropping histories (old orchards, newly planted orchards, or non-cultivated soils). The second objective of this study was to quantify populations of the root-lesion nematode, Pratylenchus, in soil and roots of plants grown in all 18 soils. Although many fungal plant pathogens are also associated with replant stress (Sewell 1981; Utkhede and Smith 1991; Dullahide et al. 1994; Gu and Mazzola 2003; Manici et al. 2003), plant-parasitic nematodes, such as Pratylenchus spp., are arguably easier to identify and isolate (Forge et al. 2016). This study presented a unique opportunity to assess the incidence of Pratylenchus spp. in arable soils in the Okanagan Valley, as these organisms are a common culprit of replant stress in young trees, and they have even been shown to affect productivity in mature trees (Santo and Wilson 1990). The third objective of this study was to measure the microbial activity in all 18 soils, as indicated by the level of fluorescein diacetate (FDA) hydrolysis. Fluorescein diacetate hydrolysis is a non-specific measure of total microbial activity in soil, as it measures the activity   31 of the enzymes esterase, protease and lyase, all of which are produced by both bacteria and fungi (Green et al. 2006; Zhai et al. 2009). Increased soil microbial activity has been shown to result in greater biological suppression to pathogens (Weller et al. 2002), such as Pratylenchus (Stirling et al. 2003). Lastly, this study aimed to develop a statistical model to determine which biotic and abiotic indicators best predict cherry growth in orchard soils of variable histories.   2.2 Materials and Methods 2.2.1 Soil sampling            In October 2015, soil was collected from 18 orchard sites, which differed in land use history, soil type, geographic region within the Okanagan Valley of British Columbia, Canada, and orchard type (Figure 2.1; Table 2.1). The orchard type was defined as ‘old’ if it previously was cropped to sweet cherry (Prunus spp.) or apple (Malus spp.), ‘new’ if it was a recently established sweet cherry orchard (< 10 yr), or 'non-cultivated' if the soil was not previously cropped with any type of fruit tree. Soil samples were taken with a shovel to a depth of 0.24 m from several locations in a 5000 m2 area until 8 L of soil were collected from each site. All soil collected from each site formed a composite sample. The soil was thoroughly mixed and passed through a 5-mm sieve to remove rocks and organic debris. Subsamples of soil from the composite sample from each site were used for preliminary analyses of soil physicochemical properties and initial populations of Pratylenchus spp. in soil.   32   Figure 2.1 Location of each of the 18 orchard sites in the Okanagan Valley from which soil was collected from (0.1 m = 50 m). Details of each site are shown in Table 2.1.  33 Table 2.1 Site information for all orchard soils used in this study, including: name, tree cultivar and rootstock planted, orchard type (old, new or non-cultivated), year current cherry trees were planted, land use history, soil type, and the latitude and longitude. Site Name Cultivara Rootstocka Orchard typeb Year current cherry trees  were planted  Land use history Soil type Latitude/Longitude 1 Carlson   N/Ac  N/A NCd N/A Non-irrigated native grasses Sandy loam 49° 40' N 119° 47' W 2 El Dorado   N/A  N/A NC N/A Non-irrigated native grasses Sandy clay 50° 2' N 119° 22' W 3 Coldstream ‘Stacatto’ Mazzard New 2015 Non-irrigated native grasses Sandy loam 50° 14' N 119° 8' W 4 Lavington ‘Skeena’ Giesela 6 New 2014 Dairy cow pastureland Sandy loam 50° 14' N 119° 6' W 5 Sutherland ‘Sweetheart’ Mazzard New 2010 Non-irrigated native grasses Sandy loam 49° 45' N 119° 46' W 6 Cholla ‘Regina’ Mazzard New  2005 Non-irrigated native grasses Loamy sand 50° 17' N 119° 27' W 7 PARC ‘Lapins’ Krymsk 5 Old 2015 Cherry orchard Loamy sand 49° 33' N 119° 38' W 8 Dendy ‘Sentennial’ Mazzard Old 2013 Apple orchard Loamy sand 49° 51' N 119° 23' W 9 Tangaro ‘Sentennial’ Mazzard Old 2012 Cherry orchard Silty clay 50° 3' N 119° 25' W 10 Bailey ‘Sweetheart’ Mazzard Old 2010 Cherry orchard Silty clay 50° 6' N 119° 23' W 11 Berry ‘Sweetheart’ Mazzard Old 2005 Cherry orchard Sandy clay 49° 54' N 119° 21' W 12 Beulah ‘Stacatto’ Mazzard Old 2005 Cherry orchard Loamy sand 49° 34' N 119° 39' W 13 Carlson ‘Sweetheart’ Mazzard Old 2005 Cherry orchard Silty clay loam 49° 36' N 119° 41' W 14 Brown ‘Stacatto’ Mazzard Old 2004 Apple orchard Silty clay 49° 34' N 119° 39' W 15 Norton  ‘Stacatto’ Mazzard Old 2003 Cherry orchard Sandy clay loam 49° 10' N 119° 34' W 16 Sidhu ‘Lapins’ Mazzard Old 2002 Cherry orchard Sandy loam 49° 34' N 119° 39' W 17 Danninger ‘Sweetheart’ Colt Old 1999 Cherry orchard Silty clay loam 49° 22' N 119° 33' W 18 Norton  ‘Sweetheart’ Mazzard Old 1994 Cherry orchard Sandy clay loam 49° 10' N 119° 34' W a = All the cultivars listed are sweet cherry (Prunus avium L.) on Mazzard (P. avium), Giesela 6 (P. cerasus x P. canescens), Krymsk 5 (P. fruticosa x P. lannesiana), or Colt (P.  avium x P. pseudocerasus) rootstocks. b = Orchard type was classified as: ‘old’ if it previously cropped sweet cherry (Prunus avium L.), or a related species, ‘new’ if it was a recently established sweet cherry orchard (< 10 yr), or 'non-cultivated' if the soil was not previously cropped with any type of fruit tree. Non-cultivated sites were subsequently planted with sweet cherry. c = No sweet cherry trees were planted at the time of soil sampling in October 2015. d = 'NC' = Non-cultivated site  2.2.2 Preliminary soil physicochemical property analysis      The day of soil sampling, a subsample of the sieved (<5 mm) soil from each site was transferred into labelled plastic drying boats and dried at room temperature for 48 h. The dried soil was ground using a mortar and pestle and sieved (<2 mm) directly into a labelled plastic bag. Electrical conductivity (EC) and pH of dried soil subsamples were measured in a 1: 2 soil: water suspension using 10 g of dry soil, and 20 ml of double-deionized H20 (ddH20) with an EC meter   34 (WTW inoLab Cond 7200), and a pH meter (Fisher Scientific™ Accumet™ XL150 pH Benchtop Meter), respectively. Permanganate-oxidizable carbon (POXC), a measure of labile carbon in the soil, was measured using 0.25 g of dry soil according to Culman et al. (2012). The amount of carbon oxidized by potassium permanganate (KMnO4) was measured colorometrically (xMark™ Microplate Absorbance Spectrophotometer). Dried soil subsamples were sent to A & L Laboratories, London, Ontario, Canada for the following measurements: Bray I- extractable P (0.03 M ammonium fluoride and 0.025 M hydrochloric acid); total C and N (Thermo Flash 2000 combustion elemental analyzer); effective CEC (0.1 M barium chloride), and exchangeable bases (Ca, Mg, K, Na) (Thermo Scientific™ iCAP™ 7200 Inductively Coupled Plasma Optical Emission Spectrometer). Dissolved organic carbon (DOC) was extracted (0.5 M potassium sulphate), and extracts analyzed using persulphate oxidation (OI-Analytical Aurora 1030W TOC analyzer). Organic matter (OM) content was determined gravimetrically by a loss on ignition procedure (%LOI).                         2.2.3 Experimental treatments and design        The remainder of the soil collected was stored at 4°C until time of planting. This stored soil from each site was either sterilized (by means of microwaving) or left untreated to assess the growth response of ‘Crimson’ sour cherry plants (Prunus ceraseus) to sterilization. To determine the influence of soil biology on plant growth, a 3-L subsample of soil (stored at 4°C for 3 months) from each site was microwaved (Citizen 700 Watt Output Microwave Oven) in clear polypropylene autoclave bags (VWR®, Radnor, PA). Microwaves cause water in the cells of soil organisms to absorb heat, which results in temperatures high enough to kill them (Trevors 1996). Soil was microwaved (700 Watts) in 500-mL increments for 4 min, shaken, and then   35 microwaved again for 4 min until an internal soil temperature of 121 °C (sterilization temperature) was reached. The internal temperature was checked by placing a thermometer into the centre of the soil sample. Microwaved soil was then stored at 4 °C overnight. The next day, soil was microwaved again using the same protocol to ensure the destruction of any spores that may have germinated after the initial sterilization process (Trevors 1996). The soil was left to cool at 4 °C for at least 1 wk before planting.         Micro-propagated ‘Crimson’ sour cherry (Prunus cerasus) explants were obtained from Agriforest Biotechnologies (Kelowna, BC, Canada). These explants had been grown in tissue culture and originated from buds containing the shoot apical meristem (Dr. Kamlesh Patel pers. comm.). At Agriforest, the buds were exposed to a proprietary regime of nutrients, hormones, and light under sterile, in-vitro conditions to produce many new plants, each a clone of the original mother plant. Once the shoots reached a height of 3-5 cm (about four wk), they were transferred onto rooting medium for 3-4 wk, and then put into potting mix in a greenhouse. The plants were moved into the greenhouse in September 2015, so they went through the dormancy stage over winter, and were approximately six months old at the time when the experiment was initated, on March 23, 2016.          At the start of the experiment, the initial shoot height of each plant was measured. Plants were planted singly in pots (9.52 cm diameter and 10.73 cm height) filled with 400 ml of soil. This was a fully factorial experiment (18 soil samples x 2 sterilization regimes x 6 replicates = 216 plants). Plants were arranged in a complete randomized block design in growth chambers (Percival Scientific Model AR36L3C8) set to the following conditions: 24°C ± 2.0°C, 35% humidity, and irradiance of 227 µmol m-2 s-1 of PAR (photosynthetically active radiation), with a 16-h photoperiod. Plants were watered to run-off as needed with distilled water and fertilized   36 every 14 d with 3 g L-1 of Pro-Gro® 20-20-20 fertilizer blend (Pro-Gro Mixes & Materials, Sherwood, OR). Plants were monitored for powdery mildew and spider mites, and sprayed with 5 mL L-1 of Green-Earth® (Langley, BC, Canada) lime-sulphur, and 20 mL L-1 of Safer’s® (Lititz, PA, USA) insecticidal soap, respectively, as needed. Plants were harvested June 1, 2017, after 10 wk of growth.                        2.2.4 Plant growth analyses          At time of harvest, shoots were cut at soil level and total shoot height increment was measured. Oven-dried shoot and root weights were determined after drying plant material at 65°C for 48 h (VWR 1305U Gravity Convection Oven, Radnor, PA). Since single replicate plants died in some soil by sterilization regime combinations, the smallest plant from all other treatments was removed and not used in further analyses, resulting in five replicates for each soil by sterilization regime combination.                     2.2.5 Nematode extraction and quantification from soil and roots     In order to confirm successful sterilization, nematodes were extracted from a subsample of sterilized soil from each site. In addition, nematodes were extracted from non-sterile soil (stored at 4°C for 3 months) two wk prior to planting to determine initial abundance of Pratylenchus in soil, and at harvest to determine the final abundance of Pratylenchus in soil. In all cases, nematodes were extracted from 50 g of soil using the Baermann pan technique (Hackenberg et al. 2000). Circular garden pot saucers (2.5 cm height; 17.5 cm diameter) were filled with water, above which a mesh filter (17 cm diameter; 75 µm holes) was placed. The mesh was covered with tissue paper (Kleenex®) and soil samples were placed on the tissue   37 paper. Water levels were monitored daily and topped up to be in line with the mesh filter. After 7 days of incubation, the nematodes in the soil moved to the bottom of the garden pot saucer, and these nematode suspensions were poured onto a No. 500 sieve (25 µm openings), washed with water, and the nematode suspensions transferred into 20-mL scintillation vials that were stored at 4°C until analysis. To determine the abundance of Pratylenchus spp. in cherry plant roots at harvest, fine roots (<2 mm) were washed with cold water prior to extraction of endoparasitic nematodes (Pratylenchus spp.) using the Petri-plate technique (Ravichandra 2014). Plastic petri plates (5 mm diameter) were filled with water, above which a mesh filter (3.5 cm diameter; 75 µm holes) was placed. Root subsamples (~2 g fresh weight) were placed on top of tissue paper, which lined the mesh filter. Incubation and harvest of nematodes from roots occurred as with soil samples. Root material from the extraction was then dried at room temperature for 72 h, and weighed in order to later determine Pratylenchus spp. g-1 dry root.       Nematodes from soil and root extracts were viewed (100x magnification) under an inverted compound microscope (Olympus CK2 Inverted Microscope). Identification of Pratylenchus spp. was based on a short stylet with basal knobs, a pharynx overlapping the intestine ventrally, and a rounded tail (Castillo and Vovlas 2007). In soil, the number of Pratylenchus spp. 50 g-1 of soil was determined, as well as total nematodes 50 g-1 of soil by counting the number of nematodes in the center column of a 12 column counting dish, and multiplying that number by 12. In roots, the number of Pratylenchus spp. g-1 dry root was determined by dividing the number of Pratylenchus spp. in a sample by the dry root weight used in the extraction. All root and soil extracts were counted within one month of extraction.     38 2.2.6 Microbial activity in soil of plants         At harvest, ~10 g (fresh weight) of soil was subsampled from each of the five replicate pots from each non-sterile treatment, and stored at 4°C. Within one month of harvest, soil microbial activity was determined by the hydrolysis of fluorescein diacetate (FDA) from each stored sample. For each sample, one gram (fresh weight) of soil was placed in a 50-mL conical tube (Falcon™) and 50 mL of pH 7.6 60 mM Na3PO4 · 12H2O (Sigma-Aldrich, St. Louis, Miss.) were added. Fluorescein diacetate (Invitrogen, CA) stock solution (0.5 mL of 4.9 mM FDA) was added to start the reaction. Blanks prepared either without the addition of the FDA solution (“no FDA” control) or without sample (“no sample” control) were measured along with one sample from each pot. The tubes were then placed in an oven at 37 °C for 3 h (VWR 1305U Gravity Convection Oven, Radnor, PA). Once removed from the oven, 2 mL of acetone (≥99.5%) (Sigma-Aldrich, St. Louis, Miss.) were added immediately to end the reaction, and tubes were shaken thoroughly by hand. The contents of the flasks were then transferred to 50-mL centrifuge tubes and centrifuged at 1725 rpm for 6 min. One-milliliter of supernatant from each sample was then measured at 490 nm (Thermo Scientific Genesys 20 Spectrophotometer Model 4001/4). Colorless FDA is hydrolyzed by both free and membrane-bound enzymes, of both fungi and bacteria, releasing fluorescein, a colored end product, which can be measured by spectrophotometry (Adam and Duncun 2001). To determine the amount of fluorescein produced by a sample, the sum of the OD490 of the “no FDA” control and the “no sample” control were subtracted from the OD490 of the sample, which accounted for any color formation that might have occurred that was not associated with the sample. The resulting OD490 value was used in the equation of the standard curve to solve for the concentration of fluorescein produced (µg g-1) (henceforth referred to as ‘FDA hydrolysis’) (Figure A.2.1). If the absorbance of the sample   39 exceeded the limits of the standard curve, the filtrate was diluted with sodium phosphate buffer (pH 7.6) until the absorbance was within the limits of the standard curve. To develop the standard curve, the absorbance of standards containing 0.03, 0.1, 0.3, and 0.5 mg of fluorescein was measured on a spectrophotometer (OD490). The standard curve was linear in this range and covered the range of FDA activity in the soils.  2.2.7 Statistical analyses           The initial shoot height was subtracted from the final shoot height to determine the total shoot height increment. The plant growth parameters statistically analyzed were shoot height increment, shoot weight, root weight, and plant weight. Shoot weight and root weight were added to form total plant weight. The orchard types 'non-cultivated’ (n=2) and ‘new’ (n=4) were pooled and the orchard types were subsequently defined as ‘new’ (n=6 orchards) and ‘old’ (n=12 orchards). The rationale for this was that 'new' and 'non-cultivated' soils had similar land use histories (i.e. un-irrigated native grasses, or dairy cow pastureland) before being converted to cherry orchards. The growth (e.g. shoot height increment, shoot weight, root weight, and plant weight) of plants planted in sterile relative to non-sterile soil from each orchard type was subjected to a blocked two-way ANOVA using GLM. The fixed factors in the model were ‘orchard type’ (old or new), and ‘sterilization regime’ (sterile or non-sterile), and the random factors were ‘site’ (18 orchard sites) and ‘block’ (six greenhouse blocks). The terms in the model were fully factorial. When there was a significant orchard type by sterilization regime interaction, Tukey’s HSD test was used to test the significance of differences (at a 5% significance level). Percent difference in growth (for each growth parameter) for plants grown in sterilized soil compared to plants grown in non-sterilized soil from each site was determined   40 using equation 2.1:  %	#$%&'ℎ	 = *+,-./	01	2.3+043	2,04 5(*+,-./	01	1,152.3+043	2,04)	(*+,-./	01	1,152.3+043	2,04)   x 100%                                           Equation 2.1   If % growth was negative (i.e. % decrease in plant growth), plants responded negatively to sterilization, while if the % growth was positive (i.e. % increase in plant growth), plants responded positively to sterilization. The average percent difference in growth of five replicate plants grown in soil from each site was determined. A one-sample t-test was used to determine if the average percent difference in growth of plants from each orchard type was significantly different from zero (at a 5% significance level).        Growth parameters of cherry plants grown in sterile relative to non-sterile soil from each of the 18 sites were subjected to blocked one-way ANOVAs using general linear model (GLM). The factors in the model were ‘treatment’ (sterile or non-sterile), ‘block’ and ‘block by treatment’. The Bonferroni correction was used in order to reduce the possibility of getting a statistically significant result when performing multiple tests (i.e. a Type I error), and therefore, the significance level for each test became P≤0.003.   Fluorescein diacetate (FDA) hydrolysis, and Pratylenchus spp. abundance in soil and roots were subjected to a blocked one-way ANOVA because these assays were performed on only non-sterile soil. The fixed factor in each model was ‘orchard type’ (old or new), and the random factors were ‘site’ and ‘block’. The terms in the model were fully factorial, however, the term ‘orchard type by block by site’ was not significant for any variables and was not included in any of the summary ANOVA tables. When there was a significant orchard type by site interaction, Tukey’s HSD (honest significant difference) test was used to test the significance of differences (at a 5% significance level).    41 Analysis of variance (ANOVA) and t-test assumptions were tested for each measured variable. The independence assumption was met since soil was randomly sampled and assigned to groups. Normality was examined using Shapiro-Wilk tests and Normal Q-Q Plots. The Levene’s test was used to assess for homogeneity of variance. Abundance of Pratylenchus in soils and roots was log (x+10) transformed and other variables that were not of equal variance and/ or from a normal distribution were log transformed. After data were transformed, each of assumptions for each test were tested again.  A Principal Components Analysis (PCA) was performed on all physicochemical variables (organic carbon (OC), carbon-to-nitrogen (C: N) ratio, total nitrogen N), total organic carbon (OC), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), sodium (Na), electrical conductivity (EC), pH, cation exchange capacity (CEC), and permanganate oxidizable carbon (POXC)); biological variables (FDA hydrolysis, Pratylenchus spp. 50 g-1 soil, and Pratylenchus spp. g-1 dry root), and topographic variables (elevation and latitude) with R (3.4.0)(see Appendix A for R code). This analysis is an investigative tool useful for identifying patterns of interrelated variables with the aim of selecting a reduced number of variables for further study (Janvier et al. 2007). The eigenvalues for each component were used to decide the number of axes to represent and display in a bi-plot on the basis of the total variance explained by each component. Loading values greater than or equal to the absolute value of 0.5 indicated significant interrelationships among variables within a principal component (Ownley et al. 2003), and any values less than this were eliminated before further analyses. Then, multivariate correlation coefficients were calculated to determine the strength of the relationships among variables. Only one variable was kept from each group of highly correlated variables, depending on which variable had the greatest collinearity tolerance level. The number of soil properties   42 influencing shoot height increment were further narrowed using the step-wise regression analysis procedure to identify a model that included the lowest number of soil properties and that best described variation in the data. Significant predictors of shoot height increment were included in the model, and non-significant variables that did not contribute any additional information for explaining and predicting the dependent variable were eliminated.       All statistical tests were conducted using SPSS Statistics version 23.0 (IBM, Chicago, IL), unless otherwise noted. All tables and figures were made in Excel (Microsoft® Excel for Mac Version 15.15.3).   2.3 Results 2.3.1 Impact of old and new orchard soils on growth of ‘Crimson’ sour cherry plants  Growth was greater in non-sterile soil from new orchards relative to non-sterile soil from old orchards for all plant growth variables (e.g. plant weight, shoot weight, root weight, and shoot height increment) (Table 2.2). Furthermore, plants grown in new orchard soils grew less after sterilization than those in non-sterilized soil, but the effect was only significant for shoot height increment and plant weight (Table 2.3). Overall, plants grown in old orchard soils had a positive growth response to sterilization, and this was seen for all of the plant growth variables (Table 2.3). However, there were exceptions to the overall pattern (Figure 2.2; Table A.2.1; Table 2.4, Table 2.5, and Table 2.6).          43 Table 2.2 Mean shoot height increment, shoot weight, root weight, and plant weight of each orchard type and sterilization regime (sterilized or non-sterilized) combination. Values represent mean (nnew=30 plants for sterilized and non-sterilized regimes; nold=60 plants for sterilized and non-sterilized regimes) and standard deviation (SD).   Plant growth measurments  Orchard Type Sterilization Regime Shoot Height  Increment(cm) SD Shoot Weight  (g) SD Root Weight (g) SD Plant Weight (g) SD  New Non-sterilized 139 a 15 0.78 a 0.21 2.3 a 0.7 3.1 a 0.8  Old Non-sterilized 99 c 19 0.65 b 0.22 1.7 b 0.8 2.3 b 0.9  New Sterilized 113 b 10 0.55 b 0.11 1.7 b 0.6 2.4 b 0.7  Old Sterilized 121 b 23 0.85 a 0.34 2.1 ab 0.8 3.0 a 0.9 ANOVA resultsb df P-value F-value P-value F-value P-value F-value P-value F-value  orchard type  1 <0.001 33 0.01 6.4 0.3 0.9 0.7 0.07           sterilization regime 1 0.5 0.3 0.6 0.2 0.5 0.3 0.5 0.4           site 17 4.1 0.06 0.02 6.8 0.2 1.7 0.5 0.9           block 5 1.2 0.3 0.08 3.7 0.1 2.5 0.4 1.2           orchard type   * sterilization regime 1 <0.001 74 <0.001 41 <0.001 13.9 <0.001 26.4                    orchard type * block 6 0.08 0.9 0.9 0.1 0.8 0.3 0.9 0.2           orchard type  * site 11 0.5 0.8 0.6 0.8 0.4 0.9 0.4 1           sterilization regime * block 5 0.9 0.06 0.6 0.7 0.7 0.5 0.7 0.5 a = Orchard type by sterilization regime combinations sharing the same letter do not differ significantly (P>0.05) according to Tukey’s HSD test. b = ANOVA results are significant at a P≤0.05 significance level.       44 Table 2.3 Percent difference in growth for plants grown in sterilized soil compared to plants grown in non-sterilized soil from ‘new’ (n=6) and ‘old’ (n=12) orchard sites. Positive values represent increased plant growth in response to sterilization, while negative values represent decreased plant growth in response to sterilization. Values represent mean percent difference in growth and standard deviation.  Percent (%) change in growth for plants grown in sterilized soil compared to non-sterilized soil Orchard Type Shoot Height Increment SD Shoot Weight SD Root Weight SD Plant Weight SD old 23.4 15.0 38.3 24.0 89.6 63.0 50.6 22.0 new -17.2 6.1 -25.5 19.0 -18.1 12.0 -22.9 12.0 T-test resultsa P-value t-value P-value t-value P-value t-value P-value t-value old (df=11) 0.008 3.2 0.03 2.4 0.02 2.7 0.01 2.8 new (df=5) 0.04 -2.6 0.09 -2.1 0.2 -1.3 0.04 -2.6 a = Percent difference in growth of plants in new and old orchard soils was significantly different from zero if P≤0.05.         Figure 2.2 Shoot height increment (cm) of plants grown in sterilized (solid box) and non-sterilized (open box) soil collected from 18 sites that were either ‘old’ (black label), ‘new’ (red label), or ‘non-cultivated’ (NC) (green) orchard types. Sites are ranked from lowest to highest growth of plants in non-sterilized soil. The asterisk (*) indicates that the mean shoot height increment was significantly different (P≤0.003) between sterilized and non-sterilized soil from the same site. Data are arranged from smallest to largest shoot height increment of plants grown in non-sterilized soil. Squares indicate mean of five plants, and bars indicate mean ± one standard deviation.     45 Table 2.4 Total weight of plants grown in non-sterile and sterile soil for each site. Values represent mean (n=5) and standard deviation (SD).       ANOVA Resultsb   Total Plant Weight (g)a  Sterilization (df=1) Block (df=5) Sterilization*Block (df=5) Orchard Type Site Non-sterile SD Sterile SD   F-value P-valuec F-value P-value F-value P-value Non-cultivated 1 3.0 0.4 2.3 0.7  1.8 0.2 3.4 0.2 0.1 0.9 Non-cultivated 2 3.3 0.7 2.1 0.6  7.7 0.05 26.1 0.1 18.5 0.1 New 3 2.5 0.6 2.1 0.7  2.6 0.1 0.41 0.7 0.2 0.7 New 4 3.4 1.0 1.8 0.3  18.6 0.003 0.04 0.9 2.5 0.1 New 5 3.0 0.7 2.0 0.6  120 <0.001 12.9 0.1 0.02 0.9 New 6 3.5 0.5 2.2 0.7  0.34 0.1 0.2 0.8 1.7 0.3 Old 7 2.5 0.2 3.2  0.8  6 0.06 39.1 0.02 0.02 0.9 Old 8 3.6 0.1 3.1 0.1  0.6 0.4 1.9 0.3 0.8 0.4 Old 9 1.8 0.7 3.8 0.6  7.5 0.02 0.5 0.6 0.8 0.4 Old 10 3.0 0.7 2.3 0.6  14.9 0.04 1.9 0.3 0.8 0.4 Old 11 4.2 0.3 2.4 0.6  16.2 0.007 0.4 0.7 0.9 0.4 Old 12 2.3 1.3 3.5 0.8  4.7 0.07 0.4 0.7 30.2 0.01 Old 13 1.9 0.9 2.2 1.0  6.2 0.03 1.1 0.4 0.7 0.5 Old 14 1.5 0.3 3.0 0.3  6.2 0.03 7.6 0.1 0.1 0.8 Old 15 1.6 0.5 2.6 1.2  6.2 0.03 0.4 0.7 6 0.5 Old 16 2.2 0.5 4.0 0.2  0.02 0.8 0.1 0.8 3.7 0.1 Old 17 2.2 0.5 2.8 0.6  5.6 0.05 1.1 0.5 0.5 0.5 Old 18 2.6 0.7 4.0 0.7  6.5 0.03 0.5 0.6 5.9 0.06 a = Total plant weight was determined by adding together shoot and root weight. b = ANOVA results compare the mean weight of plants grown in sterile or non-sterile soil from the same site. c = Results are significant at a P≤0.003 significance level due to the Bonferroni correction procedure.   Table 2.5 Shoot weights of plants grown in non-sterile and sterile soil for each site. Values represent mean (n=5) and standard deviation (SD).      ANOVA Results    Shoot Weight (g)  Sterilization (df=1) Block (df=5) Sterilization*Block (df=5) Orchard Type Site Non-sterile SD Sterile SD   F-value P-valueb F-value P-value F-value P-value Non-cultivated 1 0.91 0.1 0.43 0.1  19 0.005 2.3 0.2 0.15 0.8 Non-cultivated 2 0.72 0.07 0.67 0.1  0.47 0.5 11 0.08 0.18 0.6 New 3 0.62 0.06 0.74 0.02  2.8 0.1 11 0.04 0.18 0.6 New 4 0.8 0.1 0.41 0.07  515 0.02 5.6 0.07 0.098 0.7 New 5 0.93 0.08 0.43 0.09  33 0.001 2.7 0.2 4.6 0.1 New 6 0.67 0.03 0.48 0.05  39 0.002 0.02 0.9 21 0.04 Old 7 0.65 0.1 0.93 0.2  5.6 0.07 0.2 0.7 0.1 0.7 Old 8 0.91 0.1 0.74 0.06  4.7 0.09 0.2 0.7 0.1 0.7 Old 9 0.67 0.09 1.4 0.3  28 0.001 7.1 0.1 0.33 0.7 Old 10 0.55 0.03 0.67 0.06  128 <0.001 247 0.004 0.006 0.9 Old 11 0.92 0.1 0.65 0.2  6.2 0.04 1.6 0.3 1.3 0.4 Old 12 0.68 0.07 0.89 0.1  7.5 0.02 7.4 0.2 0.91 0.3 Old 13 0.61 0.1 0.85 0.1  7.6 0.03 0.07 0.9 0.7 0.5 Old 14 0.32 0.09 0.77 0.2  7.7 0.04 3.7 0.3 2.4 0.2 Old 15 0.55 0.1 0.91 0.2  8.9 0.04 1.9 0.3 2.6 0.2 Old 16 0.66 0.1 0.4 0.09  9.3 0.02 4.9 0.1 0.24 0.8 Old 17 0.72 0.06 0.95 0.1  9.1 0.02 0.1 0.9 0.31 0.7 Old 18 0.88 0.1 1.1 0.2   5.6 0.05 1.6 0.3 0.025 0.8 a = ANOVA results compare the mean shoot weight of plants grown in sterile or non-sterile soil from the same site. b = Results are significant at a P≤0.003 significance level due to the Bonferroni correction procedure.   46 Table 2.6 Root weight of plants grown in non-sterile and sterile soil for each site. Values represent mean (n=5) and standard deviation (SD).        ANOVA Resultsa   Root Weight (g)  Sterilization (df=1) Block (df=5) Sterilization*Block (df=5) Orchard Type Site Non-sterile SD Sterile SD   F-value P-valueb F-value P-value F-value P-value Non-cultivated 1 1.8 0.5 2.1 0.5  0.2 0.6 0.09 0.9 0.9 0.4 Non-cultivated 2 2.2 0.8 1.8 0.7  0.4 0.5 1.6 0.3 0.31 0.7 New 3 1.3 0.2 1.9 0.4  7.8 0.02 5.9 0.1 0.93 0.4 New 4 1.4 0.4 2.7 0.5  14 0.005 0.04 0.9 5.8 0.06 New 5 1.5 0.1 2.1 0.2  32 <0.001 0.77 0.5 2.9 0.1 New 6 1.7 0.5 2.8 1  4.3 0.07 0.62 0.6 3.3 0.1 Old 7 1.7 0.7 1.9 0.7  0.1 0.7 5.1 0.1 0.58 0.6 Old 8 2.3 0.9 2.7 1  0.2 0.6 2.1 0.3 0.2 0.8 Old 9 2.5 1.6 1.1 0.8  2.7 0.1 1.4 0.4 1.7 0.2 Old 10 1.7 0.3 2.5 0.3  18 0.003 8.3 0.1 0.018 0.9 Old 11 1.8 0.6 3.3 0.5  21 0.002 0.67 0.5 1.8 0.2 Old 12 2.4 0.1 1.6 0.7  5 0.05 1 0.4 19 0.009 Old 13 2.2 0.6 1.3 0.6  3.8 0.08 0.32 0.7 1.5 0.3 Old 14 1.8 0.4 1.2 0.7  1.9 0.2 1.2 0.4 0.38 0.7 Old 15 2.0 0.6 1.1 0.8  2.6 0.1 21 0.04 0.25 0.7 Old 16 1.9 0.7 1.5 0.7  0.5 0.5 0.14 0.8 3.1 0.1 Old 17 2.5 0.6 1.5 0.6  6.4 0.03 4.8 0.1 0.41 0.6 Old 18 2.8 1.2 1.7 0.2   4 0.08 2.1 0.3 2.1 0.2 a = ANOVA results compare the mean root weight of plants grown in sterile or non-sterile soil from the same site. b = Results are significant at a P≤0.003 significance level due to the Bonferroni correction procedure.  2.3.2 Microbial activity in soils from new versus old orchards     New orchard soils (non-sterilized) had nearly 2-fold greater microbial activity, as measured by FDA hydrolysis, relative to old orchard soils (Table 2.7). The difference in FDA hydrolysis among sites depended on their orchard type (Table 2.7). The sites with the highest FDA hydrolysis were Sites 5 and 1, which were new and non-cultivated soils, respectively. Site 17, an old orchard, had the lowest FDA hydrolysis.   47 Table 2.7 Mean FDA hydrolysis (µg g-1) of non-sterile soil from each site (n=5 pots for each site), and each orchard type  (nnew=6; nold= 12) after 10 wk of growth of cherry plants. Values represent mean and standard deviation (SD). Orchard Type Site  FDA hydrolysis (µg g-1)   SD Non-cultivated 1 2.8 0.01 Non-cultivated 2 2.4 0.01 New 3 2.7 0.008 New 4 2.6 0.004 New 5 2.9 0.013 New 6 2.5 0.006 Old 7 1.5 0.01 Old 8 1.9 0.009 Old 9 1.6 0.01 Old 10 1.4 0.005 Old 11 1.9 0.009 Old 12 1.4 0.004 Old 13 1.3 0.02 Old 14 1.4 0.02 Old 15 1.5 0.01 Old 16 1.6 0.008 Old 17 1.1 0.008 Old  18 1.6 0.0     Mean of Orchard Type  FDA hydrolysis (µg g-1)a SD Newb   2.7 * 0.02 Old   1.5 0.009     ANOVA Results df P-value F-value orchard type 1 0.003 11 site 17 <0.001 102 block 5 0.2 2.1 orchard type * block 5 0.9 0.001 orchard type * site 16 <0.001 110 a = The asterisk (*) indicates that the FDA hydrolysis was significantly different (P ≤ 0.05) between new and old orchard types. b = ‘Non-cultivated’ soil and ‘new’ orchard soil were pooled for ANOVA, and subsequently referred to as ‘new’.    48 2.3.3 Pratylenchus spp. abundance in roots of plants and in soils from different orchard types   Neither the population density of Pratylenchus  spp. nor the multiplication rate were different in old relative to new orchard soils at harvest (Table 2.8). The difference in abundance of Pratylenchus g -1 dry root among sites depended on orchard type (Table 2.8). In general, there was greater colonization of roots by Pratylenchus spp. if plants were grown in old orchard soil relative to new orchard soil. Most notably, Sites 1 and 2, both non-cultivated soils, did not have any observed Pratylenchus colonization in roots.   49 Table 2.8 Mean population densities of Pratylenchus spp. in soil at time of planting (n=1 for each site), and in soil and roots of plants at harvest, as well as total nematodes in soil at harvest, for each site (n=5 for each site) and orchard type (nnew=6; nold=12). Multiplication rate was determined by dividing the final population density of Pratylenchus in soil (at harvest) by the initial density in soil (at planting). Values represent means, and standard deviations (SD).   At planting  At harvest         Orchard Type Site Pratylenchus spp. 50 g-1 soil a  Pratylenchus spp. g-1 root SD Pratylenchus spp. 50 g-1 soil SD Total Nematodes  50 g-1 soil SD Multiplication Rate  SD NCb 1 0  0  0 11  4 322  166 1.3  0.9 NC 2 0  0  0 10  6 270 243 1.3  0.1 New 3 16  40 31 12  6 258  55 0.9 0.07 New 4 4  17  5 13  3 184  105 1.2  0.05 New 5 16  34  14 38  23 512  254 1.1  0.1 New 6 35  12  11 24 6 244  115 0.9 0.04 Old 7 20  74  40 7  6 278  185 0.8 0.01 Old 8 30  22  15. 11  4 176  117 0.8 0.06 Old 9 4  34  13 5.2 3 148  66 1.0 0.07 Old 10 12  33  11 25  23 164  171 1.1  0.1 Old 11 98  17  8 24  9 132  104 0.7 0.06 Old 12 2  76  60 18  6 178  141 1.3  0.09 Old 13 15  27  15 10  4 386  64 1.0 0.06 Old 14 16  31  23 17 10 188  102 1 .0 0.1 Old 15 6  151  55 17  4 458  114 1.2  0.05 Old 16 10  49  37 9  9 554  98 1.3  0.07 Old 17 7  51 25 10  4 164  36 1.1  0.7 Old 18 5  116 76 11  3 1150  185 1.1  0.01 Mean of orchard type  Pratylenchus spp. 50 g-1 soil a SD  Pratylenchus spp. g-1 root SD Pratylenchus spp. 50 g-1 soil SD Total Nematodes  50 g-1 soil SD Multiplication Rate  SD Newc  11                                                                                 12 15 ( 13.2 16  12 331 302 1.0  0.1 Old   18  25  64  55 15  10 298  191 1.2  0.1 ANOVA resultsd df   P-value F-value P-value F-value P-value F-value P-value F-value orchard type 1   0.01 8.1 0.3 1.1 0.7 0.1 0.3 1.3 block 5   0.6 0.65 0.4 1.1 0.6 0.71 0.5 0.97 site 17   <0.001 15 <0.001 4.5 <0.001 6.2 <0.001 18 orchard site * block 5   0.7 0.59 0.8 0.36 0.7 0.52 0.9 0.13 orchard type * site 16   <0.001 10 <0.001 4.3 <0.001 6.1 <0.001 18 a = No statistical analyses were completed as only one replicate soil sample from each site was used for nematode extraction.  b = ‘Non-cultivated’ orchards, which did not yet have cherry trees planted. c = Plants grown in ‘non-cultivated’ soil was pooled with soil from ‘new’ orchards.  d = ANOVA was performed on log(x+10) transformed data.  50 2.3.4 Properties of plant growth in new and old orchard soils      Physicochemical variables (organic carbon (OC), carbon-to-nitrogen (C: N) ratio, total nitrogen N), total organic carbon (OC), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), sodium (Na), electrical conductivity (EC), pH, cation exchange capacity (CEC), and permanganate oxidizable carbon (POXC)), biological variables (FDA hydrolysis, Pratylenchus spp. 50 g-1 soil, and Pratylenchus spp. g-1 dry root), and topographic variables (elevation and latitude) were used for principal components analysis (PCA) (Table 2.7, Table 2.8, and Table 2.9). The first axis of the PCA (PC1) accounted for 39.6% of the variation, and the second axis (PC2) accounted for an additional 21.3% of the variation (Figure 2.3; Table A.2.2). Latitude, POXC, and FDA hydrolysis were positively correlated in the upper left quadrant of the bi-plot, and these variables were negatively correlated with Pratylenchus g-1 root, which was the variable most correlated with PC1 and tended towards the right half of the bi-plot. In addition, elevation was negatively correlated with soil pH. The variables POXC, P, K, and total N had less influence than variables such as pH, FDA, and OM on site separation in the ordination plane. Data for new orchards clustered in the upper left quadrant of the bi-plot, along with vectors for POXC and FDA hydrolysis. The old orchards tended to be located throughout the other three quadrants. The variables total N, Pratylenchus spp. 50 g-1 soil, P, POXC, and (C: N) ratio were eliminated before further analyses (i.e. multiple regression analyses), as they had loading values less than the absolute value of 0.5 (Ownley et al. 2003) (Table 2.10).    The multivariate correlation coefficients among variables indicated that % OM and % OC were well-correlated (r=0.95). Of these variables, % OM was eliminated because it had a smaller collinearity tolerance level (0.061) than % OC (0.091). No other variables were well correlated in the PCA.              51  The number of soil properties influencing shoot height increment in the new and old orchards were further narrowed using the step-wise regression procedure to identify a model that included the least number of soil properties and that best described variation in the data. Positive predictors of shoot height increment were sodium, FDA hydrolysis, and total organic carbon (Table 2.11). Calcium and magnesium were negative predictors of shoot height increment (Table 2.11). The categorical variable 'orchard type' was a significant predictor of shoot height increment, suggesting that the effect of the explanatory variables (i.e. sodium, FDA hydrolysis, organic carbon, calcium, and magnesium) on shoot height increment depended on whether the soils were from new or old orchard soils (Table 2.11).      52 Table 2.9 Abiotic soil properties for each site were measured prior to the bioassay in December 2015. The measurements are: EC (electrical conductivity), pH, % OM (organic matter), P (phosphorus), K (potassium), Mg (magnesium), Ca (calcium), Na (sodium), C: N (carbon-to-nitrogen) ratio, TN (total nitrogen), OC (organic carbon), CEC (cation exchange capacity), and POXC (permanganate oxidizable carbon). Each chemical variable was measured on one composite sample per site. Topographic variables including, elevation and latitude, are also given for each site.   Site  EC (S m-1) pH OM (%) P (mg kg-1) K (mg kg-1) Mg (mg kg-1) Ca (mg kg-1) Na (mg kg-1) C: N TN (%) OC  (%) CEC (meq 100 g-1) mg POXC  kg-1 soil Elev-ation (m)a Latitude (°) 1 0.003 6.2 2.2 46 319 135 1170 11 9.5 0.1 1.3 9.0 697 900 49.7 2 0.01 6.8 4.0 75 553 290 1860 11 10 0.2 2.4 14.4 1094 850 50.0 3 0.2 6 9.5 102 658 280 3090 18 9.7 0.6 5.9 21.9 1731 644 50.2 4 0.08 5.8 7.2 134 357 245 2490 28 10.9 0.4 4.4 19.1 1412 573 50.2 5 0.04 4.8 3.5 335 189 115 760 14 9.1 0.2 2.2 18.5 1039 496 49.7 6 0.01 6.8 4.6 113 508 275 2070 24 11.3 0.3 2.8 15.2 1029 628 50.3 7 0.002 7 3.9 111 439 315 1810 42 10.4 0.2 2.4 14.0 435 407 49.6 8 0.02 6.4 4.0 88 540 160 1330 14 12.3 0.2 2.5 10.6 1275 498 49.9 9 0.03 7 3.8 126 572 380 2050 46 11.9 0.2 2.4 17.3 673 487 50.1 10 0.1 5.8 5.1 219 720 365 1910 38 11.5 0.3 3.2 15.8 11923 395 50.1 11 0.006 6.1 4.1 58 243 225 1640 21 11 0.2 2.5 13.2 1175 525 49.9 12 .004 6.1 3.1 41 144 150 730 19 10.6 0.2 1.9 6.5 682 500 49.6 13 0.03 6.3 8.2 224 374 280 3070 39 9.6 0.5 5.1 22.4 1529 501 49.6 14 0.04 7.7 3.1 109 686 305 3310 23 7.3 0.3 1.9 20.9 567 439 49.6 15 0.003 7.4 2.4 61 254 285 1680 24 7.6 0.2 1.5 11.5 909 339 49.2 16 0.005 6.6 2.5 51 178 140 1300 20 8.3 0.2 1.6 9.4 928 436 49.6 17 0.01 7.9 1.9 40 143 255 3850 21 6.6 0.2 1.2 21.8 595 458 49.4 18 0.06 7.5 2.7 77 248 265 1460 24 9.2 8.5 1.7 10.2 1150 339 49.2 a = Location of orchard in meters above sea level.       53   Figure 2.3 Vector loading plot of all separate variables in a two-dimensional principal components analysis (PCA) ordination of abiotic soil variables (C: N ratio, organic carbon (OC), total nitrogen (TN), POXC, phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), sodium (Na), electrical conductivity (EC), pH), biotic soil variables (FDA hydrolysis, Pratylenchus 50 g-1 soil and g-1 root), and topographic variables (elevation and latitude) for all 18 sampling sites (numbered 1 to 18). The first two axes of the PCA explained 60.9% of the variation in the variables measured. The first axis of the PCA (PC1) accounted for 39.6% of the variation. The second axis (PC2) explained an additional 21.3% of the variation.             54 Table 2.10 The proportion of the variance of each variable explained by the first two principal components. Any variables that described < |0.5| of the variation in the data were eliminated before conducting stepwise regression analyses. Variable Proportion of variance explained in principal components (%) Total nitrogen (TN) 0.22 Pratylenchus spp. 50 g-1 soil 0.30 Phosphorus (P) (mg kg-1) 0.40 mg permanganate oxidizable carbon kg-1 soil (POXC) 0.42 Elevation (m) 0.47 Carbon-to-Nitrogen ratio (C: N) 0.47 Sodium (Na) (mg kg-1) 0.52 Pratylenchus spp. g-1 root 0.56 Potassium (K) (mg kg-1) 0.56 Cation exchange capacity (CEC) (meq 100 g-1) 0.60 Calcium (Ca) (mg kg-1) 0.70 pH 0.73 Electrical conductivity (EC) (S m-1) 0.74 % Organic carbon (OC) 0.75 Latitude (°) 0.76 % Organic matter (OM) 0.76 Magnesium (Mg) (mg kg-1) 0.82 Fluorescein diacetate (FDA) hydrolysis (µg g-1) 0.83    Table 2.11 The variables that predicted shoot height increment in new and old orchard soils after conducting stepwise regression analyses. Model Variables Betab Standard Error T-value Significance Levelc Orchard typea 35.6 5.7 6.2 <0.001 Organic carbon (OC) 5.4 2.1 2.4 0.01 FDA hydrolysis 15.8 7.1 2.2 .03 Na 0.79 0.2 3.1 .003 Mg -0.09 0.3 -2.7 0.008 Ca -0.008 -3.2 -3.2 0.002 Constantd 67.1 9.7 6.8 <0.001 ANOVA Summary Degrees of Freedom F-value Significance Levele   6 36  <0.001  a = Categorical variable in model. Groups were 'new orchard soil' and 'old orchard soil'. b = The beta coefficient is the degree of change in the outcome variable for every 1-unit of change in the predictor variable. c = Variables significantly predicted shoot height increment at P≤0.05 significance level. d = The constant is the y-value in which x=0 in the equation on the regression line. e = ANOVA results are significant at a P≤0.05 significance level        55 2.4 Discussion  2.4.1 Impact of old and new orchard soils on growth of cherry plants   Previous studies have demonstrated significant promotion of plant growth when tree fruits were planted in soils that were never before cultivated (Mazzola 1999; Spath et al. 2015). In this experiment, cherry plants grown in untreated (non-sterile) soil from newly-and non-cultivated (‘new’) soils had greater plant growth relative to soil from ‘old’ orchards. When soil from new orchards was sterilized, it generally resulted in lower plant weight relative to the non-sterile counterpart. By contrast, sterilizing soil from older orchards resulted in greater plant weight relative to non-sterile soil. However, there were exceptions to this rule. For instance, sterilization of old orchard soil from Sites 8 and 11 resulted in lower shoot height increment relative to the sterilized counterpart, albeit the effect was not significant for Site 8. Furthermore, in the PCA, Sites 8 and 11 clustered in the upper left quadrant of the bi-plot with the new orchard soils. These results suggest that, in general, there may be fewer impediments to growth of cherry trees planted in new orchard soils, but if the soil health of older orchards is properly maintained, then older orchard soils can be conducive to growing cherry.                2.4.2 Abiotic and biotic predictors of plant growth        Given the complexity of soil function, it is improbable that one unique indicator can assess soil health. Therefore, in this study, to determine which soil health indicators significantly predicted shoot height increment in non-sterilized new and old orchard soils, all available biotic and abiotic variables were subjected to a data reduction technique to identify the least number of properties that best described variation in the data. The positive predictors of shoot height increment were total organic carbon, FDA hydrolysis,   56 and sodium. The variables calcium and magnesium were negative predictors of shoot height increment. Pratylenchus abundance in roots and soil was not a predictor of shoot height increment in new or old orchard soils. Each significant predictor will be discussed, but overall, further study is required to clarify and validate the results of the multiple regression models.           It is not surprising that organic carbon was a positive predictor of shoot height increment in both new and old orchard soils. Organic carbon plays a central role in determining soil physical, chemical, and biological fertility (Ferreras et al. 2006; Tejada et al. 2009; Torres et al. 2015). The quantity and quality of soil organic carbon inputs affect the activity of organisms within the soil food web, which, in turn, influences nutrient cycling, biological suppressiveness, and crop growth (Aryantha et al. 2000; Litterick et al. 2004; Fontaine et al. 2007). The organic carbon content of a soil is the result of a balance between inputs from plant roots, root exudates, and plant residues, and outputs derived from the evolution of CO2 due to respiration by soil organisms, leaching of soluble organic carbon, and particulate losses from erosion (De Ruiter 1994). However, in order to maintain the total organic carbon content of newly cultivated land, so that the above processes can take place, the input of additional organic material should be adopted (Larkin et al. 2015). Practices that increase carbon inputs, such as surface mulching, should be employed on old orchard soils, since small changes in total carbon content can have disproportionally large effects on a range of soil properties (Powlson et al. 2011).                          57  The rate of hydrolysis of FDA provides a measure of microbial activity in soil, and is thought to provide a helpful estimate of the quantity and quality of biologically available organic substances in soil, as well as indicate the level of biological suppression (Hoitink and Boehm 1999). Fluorescein diacetate (FDA) hydrolysis was a positive predictor of shoot height increment in both new and old orchard soils, and it was positively correlated with POXC, and negatively correlated with the abundance of Pratylenchus g-1 dry root in the PCA. Permanganate oxidizable carbon (POXC) reflects a readily decomposable pool of soil organic carbon (Culman et al. 2012). Therefore, soils with increased POXC may provide a readily available nutrient source for soil microorganisms, thus increasing their activity, which may create a food web suppressive to Pratylenchus. General suppressiveness results from the activities of the whole microbial community, and since multiple enzymes present in both bacteria and fungi are responsible for FDA hydrolysis, this simple method of estimating total microbial activity provides a relatively good indication of suppressiveness (Hoitink and Boehm 1999). For instance, in Stirling et al. (2003), decreased populations of Pratylenchus zeae in sugarcane soils were associated with increased microbial activity, as measured by FDA hydrolysis. However, despite the negative correlation between FDA hydrolysis and Pratylenchus g-1 root in the PCA, Pratylenchus g-1 dry root was not a significant predictor of shoot height increment in the multiple regression models.     High sodium content, or sodicity, is usually positively associated with salinity, or EC, and taken together, sodicity and salinity have been shown to have a detrimental effect on plant growth (Bonanomi et al. 2011), microbial activities (Rietz and Haynes   58 2003), and soil structure (Sumner 1995). Interestingly, in the case of this experiment, EC did not significantly predict plant growth, while Na was actually a positive predictor for both new and old orchard soils. There is no intuitive explanation for this result, as Na does not meet the strict definition of an ‘essential’plant nutrient. Nevertheless, it is considered a ‘functional’ nutrient (Subbarao et al. 2003) because it is involved in osmotic and ionic balance in plants (Subbarao et al. 2003). The problem with high Na levels in soil is that it disables the uptake of other nutrients by the plant, but fortunately, Na doesn’t usually create a problem until it is over 100 mg kg-1 (Letey 2000). In this study, there were no soils that approached that level.      Calcium and magnesium content were significant negative predictors of shoot height increment in both new and old orchard soils. The relative proportion of these elements, as well as the total amount in the soil, depends mainly on the soil parent material, drainage, and soil management practices, such as liming (Gough et al. 1994). Typically, soils with >2000 mg kg-1 of Ca can result in low plant growth, and in this study, many soils were above this level (i.e. soils from Sites 9, 13, 14, and 17) (Brady and Weil 2010). Many older orchard soils had high Ca, which coincided with lower plant growth than that of low Ca soils. In high Ca soils plant growth is usually not inhibited by Ca toxicity per se, but rather by plant deficiencies of other nutrients (i.e. potassium, phosphorus) (Brady and Weil 2010). In such soils, Ca can saturate soil colloid exchange complexes, resulting in competition among nutrients for root uptake (Brady and Weil 2010). Magnesium toxicity can occur when it is >300 mg kg-1 in soil, and again, high Mg was mainly associated with older orchard soils (i.e. Sites 9, 10, 14) (Brady and Weil   59 2010). One of the observed tendencies of high Mg soils is for them to become hardened, which can, in turn, reduce plant water uptake and water infiltration (Brady and Weil 2010). The effect of hardened soil on plant growth could have been exacerbated in this greenhouse pot experiment, as plants potted with mineral soil are already prone to forming a crusted surface layer (Brady and Weil 2010).      The fact that Ca and Mg were negative predictors of plant growth in both new and old orchard soils is likely confounded with the fact that the number of old orchard soils outweighed the number of new orchard soils in the multiple regression analysis. High levels of Ca and Mg were mainly in older orchard soils, which had lower plant growth than that of plants grown in soils with lower levels of Ca and/ or Mg. Similarly, in the PCA, the vectors for Ca and Mg were situated in the lower left quadrant of the biplot, along with soils from Sites 9, 13, and 10, which were all older orchard soils. Overall, these results suggest that a high amount of one or a few nutrients in soil does not necessarily predict greater plant growth. The balance of many nutrients in soil is likely a better predictor of plant growth (Brady and Weil 2010).  2.4.3 Pratylenchus spp. abundance in roots of plants and in soils     In my study, there was a greater abundance of Pratylenchus spp. in roots of plants grown in old relative to new orchard soil. The control of plant-parasitic nematodes in the early stages of plant growth in new orchard soils may correspond to increased orchard productivity throughout the life of these orchards, as a result of the establishment of a large network of healthy roots (Braun et al. 2010). However, in the multiple regression   60 model, Pratylenchus g-1 root was not a significant predictor of shoot height increment, nor was Pratylenchus abundance in soil.         When young cherry trees are planted into orchard soil, an initial population density of 50 to 80 Pratylenchus penetrans 100 g-1 soil can cause reduction in plant growth (Nyczepir and Halbrendt 1993). In this study, few soils reached the above density, before or after harvest. The low populations of Pratylenchus in these soils may have been due to the fact that the soil samples were kept at 4°C for three months prior to planting. Townshend (1978) reported that up to 35-days of cold storage of soil increased the root infectivity of Pratylenchus penetrans females and third stage juveniles, however, infectivity declined thereafter. In addition, although female Pratylenchus spp. can lay eggs both inside and outside the root cells, the high degree of lignification and suberization of the plant roots may have impaired nematode migration through the roots, and, in turn, nematode reproduction (Hayman, 1982).      Another factor to consider when comparing Pratylenchus root and soil abundance in the new and old orchard soils is that the species of Pratylenchus present in each of the 18 soils was unknown and not confirmed by means of molecular sequence identification. Although different species of Pratylenchus share similar morphological features, they can vary in their host range and the degree of damage they cause to a particular crop (Kleynhans et al. 1996). In this study, many of the new orchard soils were previously grasslands, and therefore, may have a high proportion of the species that colonizes grasses (e.g. Pratylenchus neglectus) (Kleynhans et al. 1996). These species are less damaging and/ or less likely to colonize the roots of tree fruits than, for example,   61 Pratylenchus penetrans (Kleynhans et al. 1996). The fact that abundance of Pratylenchus in plant roots was greater in old relative to new orchard types, despite there being no difference in soil population abundances, indicates that older orchard soils may have Pratylenchus species that have a greater root infectivity, relative to new orchard soils.  It is important to consider that lower growth of plants in old orchard soils may have also been associated with other plant-parasitic nematodes and/ or a fungal complex, and no effort was made to identify pathogens that may have been affecting roots along with Pratylenchus. Previous studies have shown reductions in apple seedling growth when replanted in soil that previously cropped apple, or similar fruit trees, relative to growth in non-cultivated soil, and the reduced growth was associated with increased frequency in the recovery of plant-pathogenic fungi, including species of Rhizoctonia, Pythium, and Phytophthora (Mazzola 1998, 1999).              2.4.4 Final chapter remarks          Findings from this study demonstrate that (1) new orchard soils were more ‘biologically suitable’ for planting sweet cherry than old orchard soils, and (2) the lower plant growth observed in old orchard soils may have resulted from changes in the microbial community, rather than from abiotic elements in the soil environment. Furthermore, results from multiple regression suggest that orchard management practices that maintain soil organic carbon levels, and stimulate an active microbial community will benefit growth of cherry trees in both new and old orchard soils. However, further study is required to clarify and validate the results of the multiple regression models.   62 Given the number and complexity of biotic and abiotic soil health indicators (i.e. soil physics, chemistry, microbiology, pathology, etc.), more rigorous model development will be necessary in order to accurately identify useful soil health indicators at the local scale.   62 3.0 Chapter 3: The effect of organic amendments on soil health in two new and two old Okanagan Valley sweet cherry orchards over two growing seasons 3.1 Background            In the Okanagan Valley of British Columbia sweet cherry is a very economically important crop (BC Ministry of Agriculture 2015). Sweet cherry plantings have been restricted to the southern and central areas of this region, as these areas have traditionally had the right combination of weather and soil conditions to produce cherry crops (Utkhede and Thomas 1988). However, climate models predict that cherry can now be grown in the more northern and higher elevation areas of this region due to the warming effects of climate change (Neilsen et al. 2013). The models that have predicted sweet cherry range expansion have largely relied on soil physicochemical properties, and climatic factors, such as the absolute minimum winter temperature; however, soil biology in the new areas is also an important factor that has not been considered (Neilsen et al. 2014).          The soil in the new areas has never been cultivated, and there is evidence to suggest that the soil microbial community in non-cultivated soils may be suppressive to soil pathogens, and, in turn, beneficial to plant growth (Mazzola 1999). Alternatively, soil biological factors may influence cherry range expansion negatively, as the replacement of a native plant with a foreign species may change the selective pressures acting on the soil microbiome (Bakker et al. 2012; Brown and Vellend 2014). Therefore, changes in land use require proper management of plant-soil-feedbacks, which could be accomplished by use of organic amendments as an orchard floor management practice (Van der Putten et al. 2016).   Organic amendments, such as nutrient-rich and fine-textured organic mulches (i.e. composted yard-waste), are commonly applied as soil amendments with the intention of   63 improving soil organic matter (SOM), nutrient status, and biological activity (Forge et al. 2003; 2008; 2013). By contrast, coarser materials (i.e. woodchips, hay) are applied as mulches with the intention of suppressing weed growth, and regulating soil moisture and temperature (Atucha et al. 2011; López et al. 2014; Hannam et al. 2016). Both have proven to be promising alternatives to soil fumigants to promote general soil suppressiveness on replant stress-prone sites, through an overall increase in biological activity and nutrient fluxes through the soil food web (Forge et al. 2003; Forge et al. 2008; Forge et al. 2013). Increased soil food web complexity potentially suppresses pathogens, including plant parasitic nematodes and fungal pathogens, by promoting antagonism in the rhizosphere (Hoitink and Boehm 1999; Weller et al. 2002; Forge et al. 2003; Noble and Coventry 2005; Forge et al. 2008; Mazzola and Manici 2012).    Fumigation has been the traditional means of managing replant stress of tree fruits replanted into soil that once cropped the same or a related plant species; however, fumigants are being phased out of conventional agriculture, as part of the move to develop sustainable approaches to manage replant stress of tree fruits. Interestingly, these new orchard soils have never been fumigated, providing a unique opportunity to test the use of nutrient-rich compost and coarse-textured woodchip mulch as a means of retaining and encouraging beneficial soil microbes, and maintaining soil health at the onset of tree establishment.    In 2015, soil management trials were established at two young, north Okanagan cherry orchards and two older, central Okanagan cherry orchards. At all of the orchards, soil organic amendment treatments were applied. Our objective was to determine whether these amendments affected soil biotic and abiotic properties. In addition, soil was collected from the amendment treatments at the four orchards. Half of the soil collected from each treatment was sterilized, and the rest was untreated, in order to assess how the soil amendments (compost, mulch, or no   64 amendment) affected soil biology and, in turn, cherry plant growth in a bioassay.  3.2 Materials and Methods 3.2.1 Field experiment with organic amendments in cherry orchards 3.2.1.1 Description of research orchard sites        The study sites were located at four newly-planted orchards in the Okanagan Valley of British Columbia: two were planted at sites never before used to grow tree fruits (Sites 1 and 2) and two were planted on long-established, older orchard sites (Sites 3 and 4).     Site 1 (50° 14' N 119° 8' W) and site 2 (50° 14' N 119° 7' W) were located in Coldstream, BC and they both have sandy loam soils. Site 1 was former grazing pastureland that became an orchard in April 2015 when sweet cherry trees [‘Stacatto’ (Prunus avium L.) on Mazzard [(P. avium) rootstock] were planted here. Site 2 was formerly a dairy farm that became an established orchard in Spring 2014 when sweet cherry trees [‘Skeena’ (P. avium) on Giesela 6 (Prunus cerasus x Prunus canescens) rootstock] were planted here. The trees at Sites 1 and 2 were irrigated daily during the growing season, and on an as-needed basis after harvest, through 2 liter-per-hour (lph) ram line irrigation systems, or through 48 lph Maxijet microsprinker irrigation systems. Trees were fertigated in the 2015 and 2016 growing seasons with 163 g nitrogen tree-1 (as calcium nitrate) three times between mid-May and the end of June; 285 g N tree-1 (as urea) at the end of March; a 20-20-20 N-P-K (nitrogen-phosphorus-potassium) blend at 272 g tree-1 in mid-May; and two applications of magnesium (as magnesium sulphate) at a rate of 0.95 g tree-1 in May.            Site 3 (49° 51' N 119° 23' W) has loamy sand soil and it was planted with sweet cherry trees [‘Sentennial’ (P. avium) on Mazzard (P. avium) rootstock] in Spring 2013 on soil that   65 previously cropped apple. Irrigation at Site 3 was supplied through a 72 lph micro sprinkler irrigation system every 1-2 h d-1 during the growing season, and 4-5 h wk-1 after harvest. Trees were fertigated in the 2015 and 2016 growing seasons with 150 g nitrogen tree-1 (as calcium nitrate) two times between mid-May and the end of June; 250 g tree-1 of a 20-20-20 N-P-K blend in mid-May; and two applications of magnesium (as magnesium sulphate) at a rate of 0.9 g m-2 in May.             Site 4 (49° 33' N 119° 38' W) has loamy sand soil and it was planted with sweet cherry trees [‘Lapins’ (P. avium) on Krymsk 5 (Prunus fruticosa x Prunus lannesiana) rootstock] in Spring 2015 on soil that previously cropped sweet cherry. Irrigation at Site 4 was supplied through a 4 L h-1 ram line irrigation system. The duration of irrigation applied each day was scheduled according to the previous day’s evaporative demand, as measured by an atmometer (ET Gauge Co., Loveland, CO). Trees were fertigated in June 2015 and 2016 with 20 g P tree-1 (as phosphoric acid), 35 g nitrogen tree-1 (as calcium nitrate), which started after P-fertigation, and ran once wk-1 for 6 wk, and 20 g potassium tree-1 (as K-Mag® potassium fertilizer) was applied in June. ‘Skeena’ on ‘Giesela 6’ (at Site 2) has a similar fruit ripening period to ‘Lapins’ on ‘Krymsk 5’ rootstock (at Site 4) and ‘Sentennial’ on Mazzard rootstock (at Site 3) has a similar fruit ripening period to ‘Stacatto’ on ‘Mazzard’ rootstock (at Site 1) (Denise Neilsen pers. comm.).            The pest management regime at Sites 1 and 4 was different from Sites 2 and 3, since Sites 1 and 4 were non-bearing orchards. However, rotating fungicides, herbicides, and insecticides of differing chemistries were sprayed for powdery mildew, weeds, and black cherry aphids at all orchard sites. At Sites 2 and 3, rotating insecticides and fungicides of differing chemistries were sprayed for spotted wing Drosophila and fungi Monilinia, Alternaria, and Botrytis.    66 3.2.1.2 Experimental design at each orchard site        Sites 1, 2, and 3 consisted of a split-plot design made of 36 plots; each plot had two measurement trees flanked by two guard trees (Table 3.1). Whole-plots consisted of two irrigation treatments: full irrigation and reduced deficit irrigation, each replicated 6 times. Within whole-plots, there were three soil amendment sub-plots: compost, mulch, and non-amended. Reduced deficit irrigation came into effect in August 2016 (after harvest) at Sites 2 and 3. Although the effect of the irrigation treatments on the measured variables was not included in the statistical model, all 12 soil amendment plots were included for statistical analyses. The experiment at Site 4 consisted of a split-split-plot design made of 72 plots; each plot had three measurement trees flanked by two guard trees (Table 3.1). Similarly, a guard row of trees was also planted at each end of the orchard block. There were six rows and each row was a whole-plot which consisted of fumigation effects: fumigated or, non-fumigated. Before trees were planted, the fumigant, Basamid®, was incorporated into three of the six rows (each row was 1.5 m wide x 74 m long) using a fertilizer spreader with an application rate of 5.43 kg plot-1 (4.9 kg 100 m-3). Dazomet is the active ingredient in Basamid®, and in moist soils it works by producing methyl-isothiocyanate upon decomposition, which is toxic to soil organisms. Within whole-plots, there were compost effect sub-plots: compost or no compost. Within sub-plots, there were legacy effect sub-sub-plots: ‘legacy’ effects of historical organic mulch applications plus annual P-fertigation, and a non-treated control. The ‘legacy’ mulch plots had been treated with shredded bark ten years previously; at the time of replanting in 2015, the residual mulch was incorporated into the soil.     67 3.2.1.3 Organic amendment information and application rates      ‘Glengrow’ compost (Glenmore Landfill, City of Kelowna, BC) and ‘Douglas-fir’ (Pseudotsuga menziesi) woodchip mulch were surface applied to plots at Sites 1, 2, and 3 in July 2015, and again in May 2016. Feedstocks for ‘GlenGrow’ compost consisted of yard trimmings, such as grass and plant debris. ‘Douglas-fir’ mulch was a by-product of the forest industry in the area, and was sourced from local distributers (Pryce Landscape Products, Vernon, BC for Sites 1 and 2; Better Earth Garden Centre, Kelowna, BC, for Site 3). At Site 4, ‘BigHorn’ compost (Big Horn Contracting Ltd., Okanagan Falls, BC) was incorporated into the soil only at the time of planting (April 2016). Feedstocks for ‘BigHorn’ compost were agricultural wastes, primarily beef feedlot waste and grape pomace. Application rates of compost and woodchip mulch at all four sites are shown in Table 3.1. Analytical nutrient results for GlenGrow compost were supplied by the City of Kelowna. Nutrient analyses for BigHorn compost were done by A & L Canada Laboratories Inc. (London, ON), and on the ‘Douglas-fir’ mulch by the BC Ministry of Environment Analytical Lab (Victoria, BC) (Table 3.2). The carbon-to-nitrogen (C: N) ratios of the Better Earth and Pryce woodchip mulches were approximately 10-fold and 8-fold higher, respectively, than that of the GlenGrow and BigHorn composts. In addition, GlenGrow compost had 9-fold higher available P relative to BigHorn compost, and 13-fold higher available P than that of BetterEarth and Pryce woodchip mulches. No information was available for the application rate and nutrient content of the ‘legacy’ mulch applied to soil at Site 4.   Nitrogen was applied by means of fertigation and <10% of the total nitrogen in the two composts was assumed to be “available” (Gale et al. 2006). Nutrient additions from woodchip mulch were negligible, as only a small fraction of this material had been incorporated into the soil. Therefore, trees in the compost plots received ~ 10% more nitrogen than trees in bare and   68 mulch plots.  Table 3.1 Orchard type, plot size, and compost and mulch application characteristics for all four sites. ‘Glengrow’ compost and the woodchip mulches (‘Better Earth’ and Pryce’) were applied to Sites 1, 2, and 3 in Spring of 2015 and 2016, and ‘BigHorn’ compost was applied to Site 4 in Spring of 2015 only.       Compost  Mulch Site Orchard Type Trees/ plot Distance between trees in  row (m)   Application area of amendment/ plot (m2  plot-1) Typea Compost required/ plot (m3 plot-1)b Surface applied to soil, or incorporated into soil?  Typec Mulch required/ plot (m3 plot-1)b Surface applied to soil, or incorporated into soil? 1 New 4 2.2 8.8 GG 0.44 Surface  DF - P 0.44 Surface 2 New 4 2.4 9.6 GG 0.48 Surface  DF - P 0.48 Surface 3 Replant 4 2.7 10.8 GG 0.54 Surface  DF - B 0.54 Surface 4 Replant 5 1.5 4.5 BH 0.90 Incorporated  DF N/Ad Incorporated a = GG (‘GlenGrow’ compost) or BH (‘BigHorn’ compost) b = Mulch and compost applied to each plot calculated assuming a 0.05 m application depth. c = DF-P (‘Douglas-fir’ Pryce’) or DF-B (‘Douglas-fir’ Better Earth) d = Mulch was not applied to Site 4 upon re-planting in April 2015, as the ‘legacy effect” of previous woodchip mulch treatment was being used for study. The source of the Douglas-fir mulch at Site 4 is unknown.        69 Table 3.2 Analytical results for ‘Glengrow’ and ‘BigHorn’ composts, and the Douglas-fir woodchip mulches.   Amendmentsa      Parameter Units ‘GlenGrow’ Compost 2015 ‘GlenGrow’ Compost 2016  ‘BigHorn’ Compost 2015 ‘Better Earth’ Mulchd ‘Pryce’ Mulchd C: N - 15:1 16:1 15:1 157:1 120:1 Foreign Matter % <1 <1 N/A N/A N/A Moisture % 35.4 37.5 6.1 N/A N/A Total Nitrogen % 1.4 1.4 1.2 0.3 0.4 Organic Matter % 39.5 43.5 30.2 81 82 Phosphorus, available mg kg-1 5000 5000 532 400 360 Potassium, water soluble mg kg-1 12500 11000 5632 610 620 ECb S m-1 0.24 0.13 0.31 N/A N/A pH pH units 8.0 8.4 7.4 N/A N/A NH4-Nitrogen mg kg-1 171 51 N/A N/A N/A NO3-Nitrogen mg kg-1 N/Ae 236 64 N/A N/A Aluminum mg kg-1 N/A N/A N/A 2044 1304 Arsenic mg kg-1 4.6 4.27 N/A N/A N/A Boron mg kg-1 26.1 33.9 4.8 3.5 4.0 Cadmium mg kg-1 0.4 0.5 N/A N/A N/A Calcium mg kg-1 21150 23500 3269 9630 7250 Chromium mg kg-1 20.8 17.7 N/A N/A N/A Cobalt mg kg-1 5.1 4.5 N/A N/A N/A Copper mg kg-1 77.7 60.8 1.4 64.7 83.7 Lead mg kg-1 18.1 18 N/A N/A N/A Mercury mg kg-1 0.1 0.1 N/A N/A N/A Molybdenum mg kg-1 2.7 2.2 N/A N/A N/A Nickel mg kg-1 12.6 11.6 N/A N/A N/A Selenium mg kg-1 0.3 0.3 N/A N/A N/A Sodium mg kg-1 505 558 346 N/A N/A Zinc mg kg-1 140 133.5 N/A 56.5 158 Magnesium mg kg-1 N/A N/A 938 1120 920 Sulfur mg kg-1 N/A N/A 40 N/A N/A Manganese mg kg-1 N/A N/A 52 204 194 Iron mg kg-1 N/A N/A 113 2486 2009 CECc meq 100 g-1 N/A N/A 40 N/A N/A a = Data expressed on a dry weight basis b = EC (electrical conductivity) c = CEC (cation exchange capacity) d = 'Douglas-fir' woodchip mulch from ‘Pryce’ and ‘Better Earth’ was ground to sawdust for analyses. e = N/A (not available) indicates analyses was not done.          70 3.2.1.4 Soil and root sampling  3.2.1.4.1 Baseline soil sampling         Soil was sampled in June 2015, before organic amendment addition, to provide a baseline measurement of the physicochemical status at each of the four orchards (hereafter referred to as baseline samples). Ten soil cores (2-cm diameter sampling tube to a depth of 30 cm) were taken from five of the rows destined to be rows included in the experimental treatment plots. Two cores were taken from each of the five rows. At Site 4, since the experimental treatments were already in effect, samples were taken from control plots (i.e., those that had not been fumigated or composted, and that had no ‘legacy effect’ treatments). Soil from each core was separated into labelled bags by depth (0 cm - 15 cm and 15 cm - 30 cm fractions) and brought back to the lab in coolers. Soil was sieved (5 mm sieve), dried at room temperature for 48 h and, then ground and sieved (<2 mm). Subsamples were analyzed for Bray I-extractable P, CEC, exchangeable bases (Ca, Mg, K, Na, Ca, Mg, K, Na), and organic matter by A&L Laboratories. Methods used for all soil physicochemical analyses are described in Section 2.3.2.  3.2.1.4.2 Soil sampling in experimental plots   Soil sampling was done within experimental plots at all four sites in October 2015 and October 2016. At sites 1, 2, and 3, a total of four soil cores, and at Site 4, a total of three soil cores, were taken 30 cm from each measurement tree of each plot with a soil corer (2-cm diameter sampling tube to a depth of 30 cm). Cores from each plot were combined to form a composite sample. Fine roots (~20 cm total length) were collected separately using a hand trowel. Roots were collected from three different locations at distances of 30 cm from each tree, and at a depth of 5 – 30 cm. Soil and roots were put into labelled bags in the field, and brought   71 back to the lab in coolers. There, roots were washed free of adhering soil and stored in 70% ethanol until analysis, and soil was sieved (5-mm sieve). Soil subsamples were taken from the soil composite samples from each plot at each site and were stored frozen (-20 °C), fresh (4 °C), or dried (at room temperature). Until further processing, frozen soil was stored for up to 3 months, fresh soil was stored for up to 2 months, and dried soil was stored for up to 4 months. In October 2015, I conducted the soil and root sampling and analyses from each plot at the four sites, and in October 2016, sampling and analyses was conducted by Tirhas Gebretsadikan and me.                          3.2.1.5 Soil abiotic and biotic property analyses 3.2.1.5.1 Soil physicochemical property analysis The same day as soil was collected, 1 g of soil from each plot was used to determine the gravimetric water content (GWC) of each sample. Aluminum plates were weighed, soil was added to the plates, and the weight of the wet soil and the plate combined was recorded. These aluminum plates were placed in a drying oven at 108 ̊C for 48 h. The dry soil and plate weight were recorded and the percent gravimetric water content for each sample was calculated using equation 3.1:  %"#$ = ('()*+,	.+*	/01()3('()*+,456	/01()('()*+,456	/01()3'()*+	.+178* ∗ 	100%                                Equation 3.1 The day of soil sampling, a subsample of the sieved (<5 mm) soil from each plot was transferred into labelled drying boats and dried at room temperature for 48 h. The soil was ground using a mortar and pestle and sieved (<2 mm) directly into a labelled plastic bag. Electrical conductivity (EC), and pH of a soil subsample were measured in a 1:2 soil: water suspension using 10 g dry soil, and 20 ml of ddH20 with an EC meter (WTW inoLab Cond   72 7200), and a pH meter (Fisher Scientific™ Accumet™ XL150 pH Benchtop Meter), respectively. Permanganate oxidizable carbon (POXC), a measure of labile carbon in the soil, was determined using a 0.25-g dry soil subsample from each composite sample from each plot according to Weil et al. (2003). The amount of carbon oxidized by KMnO4 was measured colorometrically (xMark™ Microplate Absorbance Spectrophotometer). Soil subsamples were sent away (A&L Laboratories, London, Ontario, Canada in 2015; and to the British Columbia Ministry of Environment, Victoria, B.C, Canada in 2016) for the following physicochemical parameters measurements: Bray I-extractable P, total carbon and nitrogen, inorganic carbon, CEC, exchangeable bases (Ca, Mg, K, Na), available nitrogen, dissolved organic carbon, and organic matter. For methods used for all soil physicochemical analyses see Section 2.3.2.  3.2.1.5.2 Microbial activity in soil          Soil microbial activity was measured on sieved  (<5 mm), fresh (stored at 4 °C) soil samples within 2 months after sampling in both sampling years using the fluorescein diacetate (FDA) hydrolysis method (Green et al. 2006; Zhai et al. 2009). Average FDA hydrolysis of three replicates from each composite sample from each plot at each site were calculated. See section 2.3.6 for the FDA hydrolysis method.   3.2.1.5.3 Nematode extraction and quantification in soil and roots     Nematode extractions from soil and roots from each site were done in October 2015 and 2016. All root and soil extractions were completed within 2 wk of soil collection from each site, and all nematode counts were completed within 2 months of extraction from each site. However, the methods used for extraction were different between Sites 1-3 and Site 4. At Sites 1, 2, and 3,   73 soil from each plot was sieved (5-mm sieve) to remove debris and large roots. Fine roots (< 2 mm diameter) were washed with water and used for endoparasitic nematode extraction (Pratylenchus spp.) using the Petri-plate technique (Ravichandra 2014), while 50 g of soil were extracted using the Baermann-pan technique (Hackenberg et al. 2000). See section 2.3.5 for Petri-plate and Baermann pan nematode extraction techniques.       At Site 4, soil from each plot was sieved (5 mm sieve) to remove debris and large roots and the fine roots that did not fit through the sieve were washed with water and used for endoparasitic nematode extraction using the shaker agitation technique (Shurtleff and Averre, 2005). Cleaned fine root subsamples (~ 2 g) were placed in 200-mL Erlenmeyer flasks filled with approximately 50 mL of water. Flasks were shaken at 120 rpm on a rotary shaker at room temperature. After seven days of incubation, the roots were washed with water on a No. 500 sieve (25-µm openings), and nematodes were transferred into 20-mL scintillation vials. Root subsamples from the extraction were left to dry at room temperature for 72 h, and weighed in order to later determine Pratylenchus g-1 dry root. Nematodes were extracted from 100 g of freshly sieved (<5 mm) soil by the sucrose centrifugation technique (Viglierchio and Schmitt, 1983). Soil was placed into a 2-L pitcher, and as 1.8 L of tap water were added, the soil-water mixture was stirred. The mixture was allowed to settle for 30 s before the supernatant was poured through a No. 35 (447-µm openings) sieve. Then, the contents of the first pitcher were immediately poured over a No. 400 (38-µm openings) sieve kept at a 45° angle to allow the water to filter through, while not allowing the water to overflow the edge of the sieve. More water was then rinsed at the top of the sieve, so as to carry the nematodes in the sample to the bottom wedge of the angled sieve. The remaining nematode-containing residue on the sieve was then funneled into a 50 mL plastic centrifuge tube. Tubes were centrifuged for 5 min at 3000   74 rpm, the liquid decanted off the top of the tube, and the 50-mL tube was filled to volume with sucrose (454 g sucrose L-1 water) and shaken until the soil pellet at the bottom of the tube was re-suspended. The tube was then centrifuged again for 1 min at 3000 rpm, decanted onto a No. 500 sieve (25 µm openings), and nematode samples rinsed into 20-mL scintillation vials.   Samples were stored at 4°C until analysis of population densities of Pratylenchus spp. in soils and roots. Nematodes were viewed (100x magnification) by direct examination under a microscope in a counting dish placed under an inverted microscope (Olympus CK2 Inverted Microscope) for identification based on morphological features. Individuals were identified as Pratylenchus on the basis of three lip annules, short stylet with basal knobs, pharynx overlapping the intestine ventrally, and rounded tail (Castillo and Vovlas 2007). The total nematodes 50 g-1 of soil for Sites 1 to 3, or 100 g-1 of soil for Site 4, were calculated by counting the number of nematodes in the middle column of a 12-column counting dish, and then multiplying that number by 12.  3.2.1.5.4 Percent colonization by arbuscular mycorrhizal fungi      Percent root colonization by AMF was determined using the magnified intersections method (McGonigle et al. 1990). Preserved roots (those stored in 70% ethanol) from each plot were washed with water, cut into 25 x 1 cm fragments, sandwiched between two layers of cheese cloth, and put into root staining baskets. The baskets were then soaked for 24 h in 10% KOH (Fisher Scientific, Hampton, NH). After 24 h, the KOH solution was removed and replaced with fresh 10% KOH solution. Roots were then heated in the 10% KOH solution in a 95 °C water bath for 1 h. The KOH solution was later removed and the roots were washed with de-ionized water. Next, the roots were soaked in 3% H2O2 (Fisher Scientific, Hampton, NH) solution for 30-  75 45 min at room temperature until roots were completely colorless. Following a rinse with de-ionized water, roots were soaked in 5% HCl solution (VWR®, Radnor, PA) for 5 min and then in 0.001% Trypan Blue solution for 24 h, both at room temperature (Vierheilig et al. 2005). Roots were mounted with polyvinyl alcohol-lactic acid-glycerol (PVLG) (VWR®, Radnor, PA) on a microscope slide, aligned parallel to the long axis of the slide, and covered with a coverslip. Slides were observed at 200x magnification with a compound microscope (Nikon Eclipse E800). The field of view of the microscope was moved using both traverse knobs to make one-five passes across each root segment. The distance between passes was kept constant for each sample with the aid of the stage graticule. The position on the root surface at which the centre of the eyepiece crosshairs intersected with the long axis of the root was taken as the point of intersection. Rotation of the vertical crosshair ensured that each intersection was at a 90° angle to the long axis of the root. All intersections between roots and the vertical eyepiece crosshair were examined for the presence or absence of AMF structures (arbuscules, vesicles, and hyphae). Percent AMF colonization for each root subsample was calculated as the number of AMF structures divided by 100 intersections, the total number of intersections examined for each sample.  3.2.1.5.5 Estimation of total fungi and total bacteria in soil      Real-time PCR was used to quantify the abundance of bacterial and fungal DNA in a subsample of soil from each plot. DNA was isolated from 0.25 g of frozen soil (- 20 °C subsamples) per plot using a PowerSoil DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA). The concentration of DNA was determined using a Nanodrop spectrophotometer (Thermo Scientific, Wilmington, DE). Twenty µL of DNA were aliquoted into five cryovials to prevent   76 DNA degradation during the thawing and freezing cycle. The samples were stored in a -80 ̊C freezer until used for quantification. Quantification of total bacterial and fungal DNA was determined by comparison of the cycle threshold or CT (the number of cycles to detect a signal from sample DNA) to a standard curve.  3.2.1.5.5.1 Generation of standard curves for qPCR      An Escherichia coli strain harboring a plasmid coding for the fungal 18S rRNA (pJ201-F1-1) fragment and another E. coli strain harboring a plasmid that coded for bacterial 16S rRNA (pJ201-16S) were synthesized by DNA 2.0 (Menlo Park, CA) and stored at -80 ̊C until needed. The fungal 18S rRNA fragment was amplified from Fusarium oxysporum F1-1, an isolate obtained from the roots of ‘Skeena’ variety of sweet cherry on ‘Giesela 6’ semi-dwarfing rootstock and that had previously been identified by ITS sequencing (Watson et al. 2017). Strains harboring sequences coding for bacterial and fungal plasmids were thawed and  streaked onto solid Luria Broth (LB) medium (10 g of tryptone, 5 g of yeast extract, 10 g of sodium chloride and 15 g of agar L-1) agar with 50 mg mL-1 kanamycin as a selection for both pJ201-16S and pJ201-F1-1. The plates were incubated at 37 ̊C for 24 h. Single colonies were picked and inoculated into liquid LB medium with kanamycin. Cultures were shaken at 200 rpm and 37 ̊C for 24 h and the plasmids isolated using an Omega plasmid kit 1 (Norcross, GA). The concentration of the plasmids was determined by Nanodrop spectrophotometry. Each plasmid was linearized using 1 µL of EcoRI (NEB Biolabs, Ipwich, MA) and 4 µL of EcoRI buffer (NEB Biolabs, Ipwich, MA) for pJ201-16S and 1 µL of XmaI (NEB Biolabs, Ipwich, MA) and XmaI buffer (NEB Biolabs, Ipwich, MA) for pJ201-F1-1. Sterile water was added to each digest to a total volume of 40 µL. All tubes were incubated at 37 ̊C for 1 h, and then at 65 ̊C for 20 min to   77 inactivate the EcoRI and XmaI enzymes. The products were analyzed on a 1% agarose gel to confirm linearization. The linearized plasmid was purified using QIAquick PCR Purification Kit (QIAGEN, Frederick, MD). Nanodrop spectrophotometer readings were taken in triplicate for each purified product and the average used to calculate the amount of solution containing 1,000,000 copies. The amount of liquid calculated for 106 copies of template DNA from the 16S and 18S rRNA regions of bacteria and fungi, respectively, was aliquoted into labelled cryovials and was frozen at -80 ̊C until needed.   3.2.1.5.5.2 qPCR temperature profiles and reaction mixtures      The total abundance of bacteria was estimated by real-time qPCR using the primer set BACT1369F (5’-CGGTGAATACGTTCYCGG-3’) / PROK1492R (5’-GGWTACCTTGTTACGACTT-3’) to amplify a portion of the 16S rRNA region (Suzuki et al. 2000). The temperature profile was 95 °C for 2 min, 40 cycles of 95 °C for 30 s and 56 °C for 30 s, followed by one cycle of 65 °C for 5 s and 95 °C for 50 s. The abundance of total fungal DNA was determined by amplification of the 18S rRNA region using the primer set FR1 (5’-AICCATTCAATCGGTAIT-3’) / FF390 (5’-CGATAACGAACGAGACCT-3’) (Prevost-Boure et al., 2011). The temperature profile was 95 °C for 2 min, 40 cycles of 95 °C for 15 s and 59 °C for 1 min, followed by one cycle of 70 °C for 60 s.         For the standard curve, each reaction contained 10.0 uL of SsoFast™ Evagreen® Supermix, 1.6 µL of 0.1 µM of appropriate forward and reverse primer, and 5.0 uL of template DNA of a serial dilution (106–101 gene copies reaction−1), brought up to a reaction volume of 20.0 uL using PCR grade water (Invitrogen, Carlsbad, CA). For the environmental DNA samples, each reaction contained 10 µL of SsoFast™ Evagreen Supermix (Biorad, Hercules,   78 CA), 1.6 µL of 0.1 µM primer, 1.5 µL of T4 gene 32 protein (NEB Biolabs, Ipswich, MA), and 2 µL of DNA sample, brought up to a reaction volume of 20 µL with PCR grade water. Environmental DNA samples were diluted 1000-fold for quantification of total bacteria and fungi.             Reactions were checked for amplification specificity by analysis of melting curves for a single peak, as well as confirmation of a single band of appropriate size when analyzed on a 1% agarose gel (data not shown). Quantification of each gene region was determined by comparison of the CT (cycle threshold) to a constructed standard curve. The abundance of total bacteria and fungi was reported as 16S and 18S copy number g-1 of soil, respectively. For quantification of total bacteria, the BACT1369F/ PROK1492R primer set provided amplification efficiencies of 95.0–100.5%, with R2 values that ranged from 0.994 to 0.999 (data not shown). For quantification of total fungi, the FF390/ FR1 primer set provided amplification efficiencies of 85.7–95.5%, with R2 values that ranged from 0.991 to 0.999.  3.2.1.5.5.3 Calculations to obtain copy number per g dry soil      To calculate the amount of dry soil used for each DNA extraction, equation 3.2 was used, and the copy number for each gene g-1 of dry soil was determined by equation 3.3. <	=>	?@A	B=CD	>=@	EFG	HIJ@KLJC=M = (<	=>	NHJ	B=CD	>=@	EFG	HIJ@KLJC=M ∗ (100 − %	"#$)                    Equation	3.3: L=[A	#<	?@A	B=CD = ]JK@JCM<	^_KMJCJA	`HKM ∗ HD_JC=M	>KLJ=@ ∗ ?CD_JC=M	>KLJ=@K`=_MJ	_BH?	KB	JH`[DKJH	CM	^a$b?@A	B=CD	_BH?	CM	CB=DKJC=M	 < 																																								Equation	3.3:	    3.2.1.6 Statistical analysis           Sites 1, 2, and 3 had a different experimental design relative to Site 4, and thus were analyzed separately. The irrigation treatments were not considered at any of the sites, allowing   79 there to be 12 replicates of each soil treatment per site. To compare treatment differences at Sites 1, 2, and 3, the effect of treatment on the measured variables was analyzed using repeated measures, blocked, one-way analysis of variance (ANOVA). The repeated measure was sampling year, the main factor was treatment, and the random factor was site. Terms in the model were fully factorial. If there were significant main factor amendment, or year by amendment interactions, Tukey’s HSD (honest significant difference) test was used to test the significance of differences (at a 5% significance level).       To compare the effects of the amendments within each site (Site 1, 2, or 3) over time, repeated measures, blocked, one-way ANOVAs were performed. Sampling year was the repeated measure, treatment was the fixed factor, and block was the random factor. All terms in the model were fully factorial. If there were significant main factor amendment, or year by amendment interactions, a Tukey’s HSD test was used to test the significance of differences (at a 5% significance level).  All variables measured at Site 4 were analyzed using repeated measures mixed models ANOVA. Sampling year was the repeated measure; the main factors were fumigation, compost, and legacy effects; and the random factor was block. Terms in the model were fully factorial. When main factor effects of fumigation, compost, or mulch, or their interaction with sampling year were significant, a Tukey’s HSD test was used to test the significance of differences (at a 5% significance level).          Analysis of variance assumptions were tested for each measured variable at all sites. The independence assumption was met since soil was randomly sampled and assigned to treatments. Normality was examined using Shapiro-Wilk tests and Normal Q-Q Plots. The Levene’s test was used to assess for homogeneity of variance. Abundance of Pratylenchus in soils and roots was   80 log (x+1) transformed and percent AMF root colonization data were square-root transformed to correct for unequal variance and/ or non-normality. Other variables were log transformed if they were of unequal variance and/ or not normal.  After data were transformed, each assumption for each test was tested again. All tests and test assumptions were performed using SPSS Statistics version 23.0 (IBM, Chicago, IL).  3.2.2 Greenhouse bioassay using amended orchard soil 3.2.2.1 Soil sampling    Soil samples were taken from all four sites in August 2016; however, the sampling methods and subsequent sterilization were different at Site 4 compared to Site 1, 2, and 3, so they will be discussed separately henceforth. At Sites 1, 2, and 3, two soil samples were taken with a shovel (30 cm depth) from both measurement trees within sub-plots (compost, mulch, or no amendment) of full irrigation main plots, until approximately 10 L of soil were collected from each treatment at each site (18 plots total). Surface mulch and compost was pushed aside before soil was sampled. At Site 4, a total of eight soil cores (3.2 cm diameter sampling tube to a depth of 23 cm) were taken 30 cm from two measurement trees of each plot. The soil from all four sites was brought back to the lab in coolers, thoroughly mixed, sieved (< 5 mm) to remove rocks and debris, and stored at 4°C for up to one wk before further processing.   3.2.2.2 Soil sterilization           Half of the soil from each treatment at Sites 1, 2, and 3 was microwaved in 500-ml increments in autoclave bags (VWR® Autoclavable Polypropylene Bags, Radnor, PA) for 4 min, followed by shaking, as many times as required for the internal soil temperature to reach 121 °C.   81 The internal temperature was checked by placing a thermometer into the centre of the soil sample. Microwaved soil was then stored at 4°C overnight. The next day, soil was microwaved again using the same protocol. Since sterilization requires the destruction of both viable cells, and microbial spores, microwaving the soil a second time ensured the destruction of any spores that may have germinated after the initial sterilization process (Trevors 1996). The soil was left to cool at 4.0°C before planting.          Half of the soil collected from each plot at Site 4 was bagged (Fisherbrand™ Orange Autoclave Bags, Hampton, NH) for steam pasteurization (Pro-Grow Electric Soil Sterilizer Model #: SST-30, Brookfield, WI). To prepare the steam pasteurizer, layers of test soil-filled autoclave bags were sandwiched between layers of moistened field soil. The purpose of the moistened field soil surrounding the autoclave bags was to ensure even heat distribution throughout the test soil (Trevors 1996). The test soil was then steam pasteurized twice, at 72°C for 20 h for each pasteurization cycle. The pasteurizer was left ‘off’ for 12 h after the first pasteurization cycle, and then turned back on for the second pasteurization cycle. After the second pasteurization cycle, test soil was removed from the pasteurizer and left to cool at 4°C before planting.  3.2.2.3 Experimental design  Micro-propagated ‘Crimson’ sour cherry (Prunus cerasus) explants were obtained in September 2016 from Agriforest Biotechnologies in Kelowna, BC, Canada. These explants had been grown in tissue culture and originated from buds that contained the shoot apical meristem (Dr. Kamlesh Patel pers. comm.). At Agriforest, this plant tissue was exposed to a proprietary regime of nutrients, hormones, and light under sterile, in-vitro conditions to produce many new   82 plants, each a clone of the original mother plant. In culture, once the shoots reached the height of three to five centimeters, which took about four wk, they were transferred onto sterile rooting medium for another three to four wk to develop roots, after which they were transferred into potting mix in the greenhouse. The plants I received were approximately 7 wk old. At the start of the experiment, the initial shoot height of each plant was measured.     On September 6, 2016, pots (9.52 cm diameter and 10.73 cm height) were filled with 400 ml of sterilized or non-sterilized field soil (collected from Sites 1, 2, and 3) and plants were planted singly in each pot. The experiment was fully factorial (3 sites x 3 field soil treatments x 2 lab sterilization regimes x 12 replicates = 216). Plants were arranged in a completely randomized block design in a greenhouse (UBC Okanagan, Kelowna, BC, Canada) and grown at 24°C ± 10.0°C; 45-90% humidity (average daily minimum and maximum), irradiance of 524 µmol m2 s-1 of PAR (photosynthetically active radiation) and a 16-h photoperiod. Plants were watered with distilled water every two days until water holding capacity was reached. Plants were monitored daily for powdery mildew and spider mites, and sprayed with 5 mL L-1 of Green-Earth® lime-sulphur, and 20 mL L-1 of Safer’s® insecticidal soap, respectively, as needed. Plants were harvested November 14, after 10 wk of growth.        On September 8, 2016, pots (14.5 cm height; 15 cm wide opening at top and 10 cm wide tapered at the bottom) were filled with 1000 ml of soil. The experiment was fully factorial (72 field plots x 2 lab sterilization regimes = 144 plants). Plants were arranged in a complete randomized block design in a greenhouse (Summerland Research and Development Centre, 4200 BC-97, Summerland, BC, Canada). The greenhouse conditions were as follows: 24°C ± 5.0°C in temperature; average daily maximum humidity of 75% and average daily minimum humidity of 35%; and irradiance of 911	µmol m2 s-1 of PAR (photosynthetically active radiation), with a 16-h   83 photoperiod. Plants were watered with distilled water every two days until water holding capacity was reached. Plants were harvested November 17, after approximately 10 wk of growth.   3.2.2.4 Plant growth analyses          At time of harvest, shoots were cut at soil level and total shoot height increment was measured, prior to determining oven-dried shoot weight. Root systems were scanned on an Epson Expression 11000XL scanner (Epson Canada Ltd., Markham, ON) and the following parameters were measured using WinRHIZO Regular software (Regent Instruments Inc., Quebec City, QC): total root length, total root area, and percent necrotic root surface area. A ‘necrosis key’ was calibrated with a range of root color classes. ‘Necrotic roots’ were assigned as those that were black to dark brown, while ‘healthy roots’ were assigned lighter shades of brown. The program then separated the proportion of each color class in the sample so that the percent necrotic root surface area of the sample could be calculated. If root systems were too large to fit on the root scanner, they were carefully cut in half and analyzed by combining data from two independent scans. After scanning, subsamples of fine root tissue were used for nematode colonization analysis. Remaining root tissue, and shoot tissue, were oven dried at 65°C for 48 h (VWR 1305U Gravity Convection Oven, Radnor, PA) to determine dry weight. After nematode extractions were completed, roots were dried and weighed, and added to the earlier weight to calculate total root weight.   3.2.2.5 Nematode extraction and quantification in soil and roots    Pratylenchus spp. populations from the microwave and steam sterilizer treatments were determined on each sample prior to planting in order to confirm successful sterilization of test   84 soil (see section 2.3.5). At the time of harvest, nematodes were extracted from a subsample of soil (50 g) and a subsample of fine root tissue (~2 g) from each plant (section 2.3.5). The number of Pratylenchus spp. recovered in soil at harvest (Pratylenchus final, or Pf) was divided by the number of initial Pratylenchus spp. in soil at planting (Pratylenchus initial, or Pi) to determine multiplication rate of Pratylenchus.   3.2.2.6 Statistical analyses           Greenhouse experiments conducted with soil from Sites 1, 2, and 3 shared the same experimental design, while the design at Site 4 was different. Sites 1, 2, and 3 each had their own greenhouse block, and within those main blocks there were three sub-blocks, whereby soil from blocks 1 and 2 in the field formed ‘block 1’ in the greenhouse, blocks 3 and 4 in the field formed ‘block 2’ in the greenhouse, and blocks 5 and 6 in the field formed ‘block 3’ in the greenhouse. For Site 4, the greenhouse experimental blocks were the same as that in the field blocks.   The initial shoot height was subtracted from the final shoot height to determine the total shoot height increment. The plant growth parameters statistically analyzed were shoot height increment, shoot weight, root weight, plant weight, root surface area, and root length. Total plant weight was the sum of shoot weight and root weight. To compare growth of plants among field soil amendment treatments and lab sterilization regimes, plant growth data were analyzed using a blocked two-way Multiple Analysis of Variance (MANOVA) using general linear model (GLM). The main factors were field soil amendment treatment and lab sterilization regime, and the random factors were site and block (main block). Terms in the model were fully factorial. To compare Pratylenchus abundance data among field soil amendment treatments, data were analyzed using a blocked one-way ANOVA using GLM. The main factor was field soil   85 amendment, and the random factors were site and block (main block). Terms in the model were fully factorial. To compare the main factor field soil amendment treatments and lab sterilization effects for each site (Site 1, 2, or 3), plant growth data were analyzed using a blocked two-way MANOVA using GLM. Field soil amendment treatment and lab sterilization regime were the main factors, and block (sub-blocks) was the random factor. All terms in the model were fully factorial. When main factor effects of field soil amendment treatment, or their interaction with lab sterilization regime were significant, Tukey’s HSD (honest significant difference) test was used to test the significance of differences (at a 5% significance level). To compare the main factor field soil amendment treatment effects at each site (Site 1, 2, or 3), Pratylenchus abundance data were analyzed using a blocked one-way ANOVA using GLM, where field soil amendment was the main factor, and block (sub-blocks) was the random factor. All terms in the model were fully factorial. Tukey’s HSD test was used to test the significance of differences (at a 5% significance level) among field soil amendment treatments.  For Site 4, all plant growth variables were analyzed using a mixed model MANOVA. The main factors were lab sterilization regime, field soil fumigation, compost, and legacy effects, and the random factor was block. For Site 4, Pratylenchus abundance data were analyzed using a mixed model ANOVA; the main factors were field soil fumigation, compost, and mulch effects, and the random factor was block. Terms in both models were fully factorial. When main factor effects were significant for plant growth data and Pratylenchus abundance data, Tukey’s HSD test was used to test the significance of differences (at a 5% significance level).  Analysis of variance (ANOVA) and MANOVA assumptions were tested for each measured variable (Section 3.2.1.5). Abundance of Pratylenchus in soils and roots was log (x+1) transformed and percent AMF root colonization data were square-root transformed to correct for   86 unequal variance and/ or non-normality. Other variables were log transformed if they were of unequal variance and/ or not normal. After data were transformed, each assumption for each test was tested again. Relationships between continuous variables were tested using Pearson correlations after assessing test assumptions. Linearity and absence of outliers were tested using a scatterplot, and normality was assessed as previously mentioned (Section 3.2.1.5). All statistical tests and test assumptions were performed using SPSS Statistics version 23.0 (IBM, Chicago, IL).   3.3 Results 3.3.1 Field experiments: Effect of soil amendments on soil biotic and abiotic properties across Sites 1, 2, and 3          Before experimental plot establishment in June 2015, most soil physicochemical characteristics from the 0-15 and 15-30 cm soil fractions appeared to be higher at Sites 1 and 2 than at Sites 3 and 4 (Table 3.3). One exception was pH, which appeared to be higher in both fractions at Sites 3 and 4, relative to Sites 1 and 2. There was only one replicate for each, so these data could not be compared statistically, and simply provide a reference for the physicochemical status of the soil at the beginning of the field experiments.    In the compost treatment, cation exchange capacity (CEC), soil phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), sodium, permanganate oxidizable carbon (POXC), total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), and pH were higher than other treatments (Table 3.4 and Table 3.5). The effects of the amendments on K, electrical conductivity (EC), and carbon-to-nitrogen (C: N) ratio were different among sites (Table B.3.1 and Table B.3.2). Potassium (K) was higher in compost-amended soil than in other   87 treatments at Sites 1 and 2 (Table B.3.5 and Table B.3.9), while at Site 3 K content in compost-amended soil was only higher than that in the non-amended control (Table B.3.13). Sites 2 and 3 had higher EC in compost-amended soil than in the other treatments (Table B.3.9 and Table B.3.13), and at Site 1 EC was higher in compost than in mulch-amended soil (Table B.3.5). The C: N ratio was higher in compost-amended soil at Site 1 (Table B.3.6), while at Site 2, it was higher in mulch-amended soil, compared to the other treatments (Table B.3.10). At Site 3, there was no difference in C: N ratio among treatments (Table B.3.14).    Although the effect of the amendments on percent colonization by arbuscular mycorrhizal fungi (AMF) differed among sites and between sampling years, there was still an overall significant effect in which bare soil still resulted in higher AMF colonization relative to the other treatments across Sites 1, 2, and 3 (Table B.3.3). In 2015, the percent colonization of roots by AMF was higher in mulch and compost-amended soil than that in bare soil at Site 1 (Table B.3.7). By contrast, at Site 2, colonization by AMF was higher in non-amended soil compared to the other treatments, but only in 2016 (Table B.3.11). At Site 3, there was no difference in AMF colonization among soil amendment treatments in either sampling year (Table B.3.15).             There was no effect of the amendments on soil microbial activity, as measured by FDA hydrolysis, or on soil microbial abundance, as measured by bacterial 16S and fungal 18S copy number g-1 soil (Table 3.6). There was a lower abundance of Pratylenchus in soil and roots in compost-amended soil relative to the other treatments, and these results coincided with higher total nematodes in compost-amended soil than in the other treatments (Table 3.7); however, these effects were not quite significant at a <0.05 significance level.                  88  The effect of year and interactive effects of year with soil amendment and site on the measured biotic and abiotic soil properties across Sites 1, 2, and 3 are in Appendix B (Tables B.3.1-B.3.4). In addition, the effect of year and the interactive effects of year with soil amendment and block on the measured biotic and abiotic soil properties for Site 1 (Tables B.3.5-B.3.8), Site 2 (Tables B.3.9-B.3.12), and Site 3 (Tables B.3.13-B.3.16) are also in Appendix B.    Table 3.3 Baseline soil physicochemical analyses taken before experimental plot establishment in June 2015 from soil depths of 0-15 cm and 15-30 cm (n=1 for each parameter).   Site 1 Site 2 Site 3 Site 4 Parameter Units 0-15 cm 15-30 cm 0-15 cm 15-30 cm 0-15 cm 15-30 cm 0-15 cm 15-30 cm Organic Matter (OM) % 6.7 5.9 5.5 6.2 3.3 2.3 2 1.8 Phosphorus (P)  mg kg-1 136 39 192 187 67 43 171 119 Potassium (K) mg kg-1 445 342 515 297 243 146 219 170 Magnesium (Mg) mg kg-1 220 180 215 200 165 120 135 165 Calcium (Ca) mg kg-1 1870 70 2660 2360 1310 1090 1440 1280 Sodium (Na) mg kg-1 24 23 33 26 12 14 19 22 pH No units 5.4 5.7 6.2 5.9 6.3 6 6.3 6.4 Cation exchange capacity (CEC) meq 100 g-1 16 14 17.7 15.5 9.8 8.1 10.2 9.5   89 Table 3.4 Effect of soil amendment (bare, compost, or mulch) on cation exchange capacity (CEC), electrical conductivity (EC), and elements phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) among Sites 1, 2, and, 3 in October 2015 and 2016 sampling year. Values represent means (n=72) and standard deviation (SD). Degrees of freedom (df), and P-and F-values for each variable from the ANOVA are given.   Amendmenta CEC (meq 100 g-1 soil) SD P  (mg  kg-1) SD Ca (mg  kg-1) SD K (mg  kg-1) SD Mg  (mg  kg-1) SD Na  (mg  kg-1) SD EC (S  m-1) SD bare compost mulch 19.9 b 2.0 150 b 34 2993 b 419 434 b 186 293 b 80 39.5 b 8.0 0.4 ab 0.1 22.4 a 1.9 203 a 43 3123 a 280 1030 a 283 383 a 105 69.2 a 12.0 0.6 a 0.2 17.6 b 2.7 148 b 30 2936 b 306 432 b 148 286 b 82 35.2 b 5.6 0.2 b 0.1 ANOVA  Resultsb, c df P F P F P F P F P F P F P F Site 1 <0.001 318 <0.001 88 <0.001 384 <0.001 20 0.041 3.4 <0.001 194 <0.001 109 Amendment 2 <0.001 39 <0.001 27 0.018 4.3 <0.001 172 <0.001 14 <0.001 186 <0.001 40 Site by Amendment 4 0.03 2.8 0.2 1.6 0.27 1.3 <0.001 6.0 0.4 1.0 <0.001 6.1 0.2 1.4 a = Amendments sharing the same letter within a column do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values were significant at a P ≤ 0.05 significance level. c = For year by site, year by amendment, and year by site by amendment interactions see Table B.3.1 (Appendix B).   Table 3.5 Effect of soil amendment (bare, compost, or mulch) on permanganate-oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio, and pH among Sites 1, 2, and 3 in  October 2015 and 2016 sampling years. Values represent means (n=72) and standard deviation (SD). Degrees of freedom (df), and P-and F-values for each variable from the ANOVA are given. Amendmenta mg POXC kg-1  soil SD TN (%) SD TC (%) SD OC (%) SD OM (%) SD C: N SD pH SD bare compost mulch 1313 b 238 0.3 b 0.06 3.9 b 0.5 3.9 b 0.5 7.0 b 0.9 11.5 1.1 6.3 b 0.5 1503 a 272 0.4 a 0.09 4.8 a 0.7 4.8 a 0.7 8.4 a 1.2 11.6  0.8 6.7 a 0.2 1324 b 224 0.3 b 0.06 4.1ab 0.5 4.0 b 0. 7.2 b 0.8 11.6 0.8 6.4 b 0.2 ANOVA Resultsb, c df P F P F P F P F P F P F P F Site 1 <0.001 17 <0.001 177 <0.001 339 <0.001 358 <0.001 353 <0.001 30 <0.001 59 Amendment 2 <0.001 10 <0.001 26 <0.001 29 <0.001 36 <0.001 30 0.08 2.7 <0.001 10 Site * Amendment 4 0.9 0.3 0.10 2.1 0.1 2.0 0.19 1.6 0.3 1.3 0.044 2.6 0.2 1.5 a = Amendments sharing the same letter within a column do not differ significantly (P>0.05), according to Tukey’s HSD test b = Values are significant at a P ≤ 0.05 significance level. c = For year by site, year by amendment, and year by site by amendment interactions see Table B.3.2 (Appendix B).     90 Table 3.6 Effect of soil amendments (bare, compost, or mulch) on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S copy number) and fungi (18S copy number), fungal-to-bacterial (F: B) ratio, and % arbuscular mycorrhizal fungi (AMF) colonization among Sites 1, 2, and 3 in October 2015 and 2016 sampling years. Values represent means (n=72) and standard deviation  (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.  Amendmenta FDA hydrolysis  (µg g-1) SD log(16S copy #  g-1 dry soil) SD log(18S copy # g-1 dry soil) SD F: B Ratio SD % AMF Colonization SD bare compost mulch 3.7 1.2 9.1 0.3 7.7 0.4 0.8 0.05 27.9 a 9.8 4.1 1.8 9.3 0.4 7.7 0.3 0.8 0.05 21.3 b 8.2 4.4 2.0 9.2 0.4 7.8 0.3 0.8 0.05 22.6 b 8.3 ANOVA Resultsb, c df P F P F P F P F P F Site 1 <0.001 35 <0.001 18 <0.001 120 <0.001 5.3 0.001 7.7 Amendment 2 0.5 0.8 0.53 0.6 0.90 0.1 0.74 0.3 <0.001 10 Site * Amendment 4 0.6 0.7 0.88 0.3 0.41 1.0 0.79 0.4 0.04 2.7 a = Amendments sharing the same letter within a column do not differ significantly (P>0.05), according to Tukey’s HSD test b = Values are significant at a P ≤ 0.05 significance level. c = For year by site, year by amendment, and year by site by amendment interactions see Table B.3.3 (Appendix B).   Table 3.7 Effect of soil amendment (bare, compost, or mulch) on Pratylenchus spp. abundance in 1 g root and 50 g bulk soil, and total nematodes in 50 g bulk soil among Sites 1, 2, and 3 in October 2015 and 2016 sampling years. Values represent means (n=72) and standard deviation (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.  Amendment Pratylenchus spp. g-1 root SD Pratylenchus spp. 50 g-1 soil SD Total Nematode 50 g-1 soil SD bare compost mulch 133 240 23 16 232 143 103 132 18 13 289 162 161 352 21 14 251 127 ANOVA Resultsa, b df P F P F P F Site 1 0.003 6.3 0.001 8.1 <0.001 15.3 Amendment 2 0.052 2.3 0.055 2.3 0.054 3.1 Site * Amendment 4 0.2 1.6 0.3 1.3 0.2 1.3 a = Values are significant at a P ≤ 0.05 significance level. b = For year by site, year by amendment, and year by site by amendment interactions see Table B.3.4 (Appendix B).   91 3.3.2 Greenhouse experiment of plants grown in soil from Sites 1, 2, and 3 3.3.2.1 Effect of soil treatments on plant growth and Pratylenchus abundance in soil  across Sites 1, 2, and 3          Amended and non-amended field soil was collected and sterilized, or left untreated, to assess how the field soil amendment treatments influenced soil biology and, in turn, plant growth. No additional amendments were applied in the greenhouse experiment. There were no sterilization or amendment effects for plant growth across Sites 1, 2, and 3 (Table 3.8); instead, the amendment and sterilization effects depended on which site the soil was from. Therefore, amendment and sterilization regime effects within each site will be discussed in the next sections. The effect of the soil amendments on Pratylenchus spp. abundance in soil and roots of plants did not depend on what site the soil was from (Table 3.9). Across sites, there was also no significant effect of soil treatment on Pratylenchus spp. abundance in soil and roots of plants (Table 3.9). The effect of the soil amendments on total nematode abundance did depend on what site the soil was from (Table 3.9). Amendment effects for each nematode variable within each site will also be discussed in the next sections.    92 Table 3.8 Effect of the main factors amendment (compost, bare, or mulch) and sterilization regime (sterilized or non-sterilized), and the random factors site and block, on plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height increment) among plants grown in soil from Sites 1, 2, and 3. Values equal mean and standard deviation (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.   Plant growth measurements (n=6 for each treatment at each site)  Treatments   Root Length  (cm) SD Root Surface Area (cm2) SD Root  Weight (g) SD Shoot  Weight (g) SD Plant Weight (g) SD Shoot Height  Increment (cm) SD Amendment Effects              bare (n=12)  933 98 31.0 14.1 1.0 0.4 1.0 0.3 2.0 2.3 11.0 3.1 compost (n=12)  949 105 37.1 18.2 1.0 0.2 1.0 0.3 1.0 0.5 11.1 2.5 mulch (n=12)  972 69 42.0 15.5 1.0 0.3 1.0 0.2 2.0 0.4 11.2 2.1 Sterilization Effects               non-sterilized (n=18)  957 71 33.3 13.1 0.6 0.3 0.8 0.2 1.4 0.5 10.5 2.6 sterilized (n=18)  945 109 39.8 18.9 0.7 0.3 0.9 0.2 1.8 1.9 11.6 2.4 Amendment * Sterilization effectsa              bare + non-sterilized (n=6)  935 64 28.4 9.6 0.6 0.2 0.8 0.2 1.4 0.4 10.0 3.1 bare + sterilized (n=6)  931 124 34.4 17.7 0.8 0.5 0.8 0.3 2.3 3.3 11.6 3.0 compost + non-sterilized (n=6)  948 89 29.1 9.4 0.5 0.2 0.6 0.2 1.1 0.4 10.2 2.1 compost + sterilized (n=6)  949 120 43.9 22.1 0.6 0.3 1.0 0.2 1.5 0.6 12.3 2.4 mulch + non-sterilized (n=6)  987 45 42.4 14.8 0.8 0.3 0.8 0.2 1.6 0.5 11.3 2.5 mulch + sterilized (n=6)  956 85 40.9 16.2 0.7 0.2 0.8 0.1 1.5 0.4 11.1 1.7 ANOVA Resultsa df P F P F P F P F P F P F Amendment 2 0.5 0.8 0.5 0.8  0.5 0.6 0.9 0.1 0.6 0.4 0.8 0.5 Sterilization 1 0.7 0.2 0.5 0.6 0.6 0.2 0.3 1.1 0.4 1.0 0.4 0.7 Site 2 0.6 1.8 0.6 0.4 0.9 0.01 0.6 0.8 0.7 0.4 0.8 0.2 Block 2 0.7 0.4 0.8 0.1 0.8 0. 0.9 0.1 0.6 0.4 0.7 0.4 Amendment * Sterilization 2 0.9 0.06 0.5 0.8 0.4 1.0 0.3 1.4 0.6 0.5 0.4 0.8 Amendment * Site 4 0.8 0.4 0.3 1.3 0.2 1.8 0.5 0.8 0.4 1.1 0.8 0.3 Amendment * Block 4 0.7 0.4 0.3 1.2 0.4 9.4 0.5 10 0.4 1.5 0.9 0.08 Sterilization * Site 2 0.6 0.5 0.1 2.4 0.2 2.0 0.4 1.0 0.6 0.5 0.1 3.7 Sterilization * Block 2 0.7 0.3 0.9 0.05 0.6 11 0.2 2.9 0.3 2.6 0.5 0.7 Site * Block 4 0.1 2.3 0.1 2.7 0.3 1.6 0.7 0.4 0.4 1.9 0.7 0.4 Amendment * Sterilization * Site 4 <0.001 11 <0.001 12 <0.001 14 <0.001 10 <0.001 13 <0.001 10 Amendment * Sterilization * Block 4 0.1 2.5 0.4 0.9 1.0 0.04 0.6 0.6 0.4 1.1 0.05 3.8 Amendment * Site * Block 8 0.1 2.1 0.1 2.5 0.3 1.3 0.7 0.6 0.4 1.0 <0.001 8.1 Sterilization * Site * Block 4 0.1 3.2 0.3 1.4 0.3      1.2 0.1 2.1 0.6 0.6 0.4 1.1 Amendment * Sterilization * Site * Block 8 0.2 1.27 0.85 0.51 0.91 0.42 0.73 0.65 0.48 0.96 0.99 0.21 a = Results are significant at a P≤0.05 significance level.  93 Table 3.9 Effect of the main factor amendment (compost, bare, or mulch), and random factors site and block on abundance of Pratylenchus spp. in soil and roots, total nematodes in soil, multiplication rate of Pratylenchus spp., and the percent necrotic root surface area at harvest of plants grown in soil from Sites 1, 2, and, 3. Values equal means and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.    Parameters (n=6 for each variable)  Amendment   Pratylenchus  spp. 50 g-1 soil SD Pratylenchus  spp. g-1 dry root SD Total  Nematodes  50 g-1 soil SD Multiplication  Rate of  Pratylenchus  spp. 50 g-1 soilc SD % Necrotic  root  surface  area SD bare  43 21 167 199 201 71 1.4 0.7 80.8 5.1 compost  18 10 36 43 228 119 1.3 1.2 75.4 7.5 mulch  27 21 17 35 227 98 1.5 1.4 75.6 6.1 ANOVA Resultsa df P F P F P F P F P F  Amendment 2 0.1 4.1 0.1 4.1 0.7 0.3 0.9 0.3 0.2 1.9  Site 2 0.7 0.6 0.7 0.6 0.6 0.6 0.2 21 0.2 2.1  Block 2 0.4 1.2 0.5 0.9 0.7 0.3 0.8 0.3 0.6 0.6  Amendment * Site 4 0.7 0.5 0.2 1.8 <0.001 4.6 0.7 0.5 0.1 2.5  Amendment * Block 4 0.8 0.4 0.8 0.4 <0.001 4.1 0.2 1.8 <0.001 4.1  Site * Block 4 0.4 1.2 1.0 0.2 <0.001 10.5 0.4 1.2 0.1 3.8  Amendment * Site * Block 8 1.0 0.3 0.7 0.7 0.9 0.4 0.4 1.1 1.0 0.3 a = Results are significant at a P≤0.05 significance level.   3.3.3 Effect of soil treatments at each site on plant growth and Pratylenchus abundance 3.3.3.1 Site 1            Overall, soils amended with mulch produced plants that were larger than non-amended soils; however, the effect of sterilization depended on the amendment treatment (Table 3.10). In compost-amended soils, plants grew larger in pots with lower microbial activity (i.e., sterilized) than those of the untreated (non-sterilized) counterpart. By contrast, when plants were grown in non-amended (bare) soil, plant weight was lower when microbial activity was reduced by sterilization. Non-sterilized mulch-amended soil had greater total root surface area and root   94 weight than that in the non-sterilized compost and non-amended treatments.   There was no effect of soil amendment on Pratylenchus abundance in soil, total nematodes in soil, nor multiplication rate of Pratylenchus in soil (Table 3.11). However, there were fewer Pratylenchus spp. g-1 dry root when plants were grown in compost and mulch amended soil than in non-amended soil (Table 3.11). In addition, abundance of Pratylenchus spp. in roots was positively correlated with % necrotic root surface area (r=0.62; P=0.006), and negatively correlated with root surface area (r=-0.5; P=0.017) (Figure 3.1 A, B).     95 Table 3.10 Effect of soil amendment (bare, compost, or mulch) and/ or sterilization regime (sterilized or non-sterilized) on measures of plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height increment) for plants grown in soil from Site 1. Values represent means and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.  Plant growth measurements  Treatments total root length (cm) SD total surface area (cm2) SD root weight (g) SD shoot weight (g) SD plant weight (cm) SD shoot height  increment (cm) SD Amendment Effects             bare (n=12) 862 a 127 22 b 11 0.41 c 0.21                                     0.75 0.34 1.1 b 0.5 11.2 ab 2.1 compost (n=12) 923 ab 102 39 ab 22 0.67 b 0.18 0.94 0.28 1.6 ab 0.4 12.4 a 2.8 mulch (n=12) 956 a 31 51 a 12 0.87 a 0.24 0.85 0.19 1.7 a 0.4 10.8 b 2.4 Sterilization Effects              non-sterilized (n=18) 922 92 38 18 0.71  0.27 0.86 0.25 1.1  0.2 8.8 b 1.4 sterilized (n=18) 905 113 37 21 0.56  0.38 0.83 0.31 1.8  0.3 12.3 a 1.6 Amendment * Sterilization effectsa             bare + non-sterilized (n=6) 929 ab 83 29 bcd 11 0.56 b 0.12   0.94 ab 0.35 1.5 ab 0.2 11.2 b 0.9 bare + sterilized (n=6) 794 b 133 15 b 6. 0.25 c 0.17    0.55 c 0.19 0.8 c 0.4 11.1 b 1.5 compost + non-sterilized (n=6) 861 ab 113 27 cd 13 0.57 b 0.18  0.71 bc 0.10 1.3 bc 0.4 10.5 b 1.9 compost + sterilized (n=6) 984 a 32 52 ab 22 0.75 ab 0.15 1.2 a 0.1 1.9 a 0.2 14.3 a 1.4 mulch + non-sterilized (n=6) 975 a 32 58 a 12 1.1 a 0.1 0.93 ab 0.21 1.9 a 0.2 11.4 b 1.4 mulch + sterilized (n=6) 939 a 26 43 abc 9           0.72 ab  0.20 0.77 bc 0.15 1.5 ab 0.3 10.2 b 2.1 ANOVA Resultsb P F P F P F P F P F P F Amendment (df=2) 0.001 10.4 <0.001 17 <0.001 16.1 0.1 2.4 0.002 8.5 0.041 3.8 Sterilization (df=1) 0.3 0.9 0.8 0.04 0.06 4.5 0.7 0.2 0.2 1.8 0.005 10 Block (df=2) <0.001 14.1 0.1 2.4 0.4 0.7 0.9 0.07 0.7 0.3 0.5 0.5 Amendment * Sterilization (df=2) <0.001 19.0 0.001 10 0.01 6.0 <0.001 13.0 <0.001 12.1 <0.001 9.5 Amendment * Block (df=4) 0.016 4.0 0.08 2.4 0.6 0.5 0.4 0.9 1.0 0.4 0.1 1.7 Sterilization * Block (df=2) 0.4 0.7 0.5 0.6 0.8 0.2 0.05 3.9 0.2 0.1 0.9 0.7 Amendment * Sterilization * Block (df=4) 0.07 2.4 0.4 0.8 0.9 0.1 0.8 0.3 0.7 0.4 0.1 1.7 a = Soil amendment by sterilization regime interactions sharing the same letter within a column do not differ significantly (P>0.05), according to Tukey’s HSD test  b = Results are significant at a P≤0.05 significance level.  96 Table 3.11 Effect of soil amendment (bare, compost, or mulch) on population densities of Pratylenchus spp. in soil at time of planting, and Pratylenchus spp. in soil and roots, total nematodes in soil, multiplication rate of Pratylenchus spp., and the percent necrotic root surface area at harvest at Site 1. Values represent means (n=6 for each treatment) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.   At planting   At harvest         Amendmenta   Pratylenchus spp. 50 g-1 soil SD  Pratylenchus spp. 50 g-1 soil SD Pratylenchus spp. g-1 dry root SD Total Nematodes50 g-1 soil SD Multiplication Rate of Pratylenchus spp. 50 g-1 soilc SD % Necrotic root surface area SD bare  25 7  25 10 217 a 81 204 18 0.039 0.20 82.5 3.6 compost  11 7  25 10  39 b 25 208 140 0.073 0.48 75.6 11.0 mulch  15 6  21 9 3 b 8 128 30 0.14 0.32 72.6 3.6  ANOVA  Resultsb df P F  P F P F P F P F P F  Amendment 2 0.1 4.1  0.1 2.7 <0.001 22 0.2 1.6 0.5 0.5 0.06 6.2  Block 2 0.4 1.2  0.7 0.3 0.9 0.02 0.8 0.1 0.2 1.5 0.5 0.6  Amendment* Block 4 0.1 2.5  0.5 0.8 0.3 1.2 0.6 0.6 0.09 2.7 0.8 0.3 a = Soil amendments sharing the same letter within a column do not differ significantly (P>0.05), according to Tukey’s HSD test b = Values are significant at a P ≤ 0.05 significance level. c = Pratylenchus spp. 50 g-1 soil at harvest divided by Pratylenchus spp. 50 g-1 soil at time of planting.        97  Figure 3.1 a) Population density of the log (Pratylenchus spp. g-1 dry root) was positively associated with the square-root of % necrotic root surface area (n=6 for each treatment) (Pearson correlation; r=0.62; P=0.003) at Site 1. The equation of the line for the regression is y = 8.4 + 0.28x (df=17; F=10.2; P=0.006; R2=0.38). b) Population density of the log of Pratylenchus spp. g-1 dry root + 1 was negatively associated with total root surface area (n=6 for each treatment) (Pearson correlation; r= -0.5; P=0.017) at Site 1. The equation of the line for the regression is y = 49.9 – 9.3x (df=17; F=5.4; P=0.033; R2=0.25). Black circles represent non-amended (bare), blue circles represent compost-amended, and red circles represent mulch-amended soil treatments.  3.3.3.2 Site 2            At Site 2, the only effect of the field amendments was that plants grown in compost or mulch-amended soil had greater root length than those in non-amended soil (Table 3.12). Plants generally grew larger in soil with lower microbial activity (sterilized) than untreated (non-sterilized) soil, although for root weight, differences were only significant for non-amended (bare) soil. The population density of Pratylenchus spp. in soil was lower in compost than non-amended soil (Table 3.13). In addition, there were fewer Pratylenchus spp. g-1 root when plants were grown in mulch than in non-amended soil. The population density of Pratylenchus spp. g-1 dry root was positively associated with percent necrotic root surface area (r=0.5, P=0.012), and   98 negatively associated with total root surface area (r=-0.48, P=0.023) (Figure 3.2 A, B).  99 Table 3.12 Effect of soil amendment (compost, bare, or mulch) and/ or sterilization regime (sterilized or non-sterilization) on measures of plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height in increment) for plants grown in soil from Site 2. Values represent means and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.  Plant growth measurements  Treatment total root length (cm) SD total surface area (cm2) SD root weight (g) SD shoot weight (g) SD plant weight (g) SD shoot height increment (cm) SD Amendment Effects              bare (n=12) 951 b 66 36.4  5.3 0.72 0.38 0.67 0.057 1.4 0.5 11.8 1.5  compost (n=12) 999 a 18 45.6  9.4 0.62 0.22 0.74 0.26 1.3 0.4 10.6 2.8  mulch (n=12) 990 a 50 43.3  5.1 0.67 0.26 0.73 0.21 1.4 0.4 10.7 2.4 Sterilization Effectsa              non-sterilized (n=18) 959 b 65 30.2 b 8.5 0.49  0.20 0.58 b 0.13 1.1 b 0.2 8.7 b 1.4 sterilized (n=18) 1001 a 20 53.3 a 10.5 0.86  0.24 0.89 a 0.16 1.7 a 0.3               12.3 a 1.6  Amendment * Sterilization effectsb            bare + non-sterilized (n=6) 909 69 26.1 8.8 0.45 b 0.20 0.64 0.081 1.1 0.2 8.7 0.9 bare + sterilized (n=6) 992  24 46.7 13.5 0.99 a 0.31 0.83 0.18 1.8 0.4 11.8 1.5 compost + non-sterilized (n=6) 989  18 32.4 3.9 0.49 b 0.22 0.54 0.19 1.1 0.4 8.4 1.9 compost + sterilized (n=6) 1009  11 58.8 5.3 0.75 ab 0.15 0.95 0.13 1.6 0.2 12.8 1.4 mulch + non-sterilized (n=6) 978   68 32.1 11.1 0.53 b 0.21 0.58 0.11 1.1 0.2 9.1 1.4 mulch + sterilized (n=6) 1002  23 54.5 9.0 0.84 ab 0.22 0.88 0.17 1.7 0.3 12.3 2.1 ANOVA Resultsc P F P F P F P F P F P F Amendment (df=2) 0.02 4.8 0.09 2.6 0.5 0.5 0.9 0.01 0.8 0.2 0.7 0.3 Sterilization (df=1) 0.005 10 <0.001 46 <0.001 23 <0.001 34 <0.001 32 <0.001 48 Block (df=2) 0.05 3.6 0.1 2.5 0.9 0.1 0.5 0.6 0.8 0.2 0.6 0.5 Amendment * Sterilization (df=2) 0.1 2.3 0.7 0.2 0.001 10 0.2 1.5 0.9 0.1 0.5 0.6 Amendment * Block (df=4) 0.8 0.3 0.9 0.2 0.1 1.8 0.2 1.4 0.1 1.8 0.6 0.6 Sterilization * Block (df=2) 0.2 1.8 0.9 0.1 0.1 2.2 0.4 0.8 0.2 1.8 0.1 2.3 Amendment * Sterilization * Block (df=4) 0.4 0.9 0.9 0.07 0.9 0.1 0.4 0.9 0.7 0.4 0.1 1.7 a = Soil sterilization effects not sharing the same letter within a column differ significantly (P>0.05).  b = Soil amendment by sterilization effects not sharing the same letter within a column differ significantly (P>0.05), according to Tukey’s HSD test. c = Results are significant at a P≤0.05 significance level.      100 Table 3.13 Effect of soil amendment (bare, compost, or mulch) on population densities of Pratylenchus spp. in soil at time of planting, and Pratylenchus spp. in soil and roots, total nematodes in soil, multiplication rate of Pratylenchus spp., and the percent necrotic root surface area at harvest at Site 2. Values represent means (n=6 for each treatment) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.    At planting   At harvest         Amend-menta   Pratylenchus spp. 50 g-1 soil SD  Pratylenchus spp. 50 g-1  soil SD Pratylenchus spp. g-1 dry root SD Total Nematodes 50 g-1  soil SD Mult- iplication  Rate of Pratylenchus spp. 50 g-1   soilc SD % Necrotic root surface area SD bare  30 a 7  53 a 28 103 a 65 170  78 2.2 1.2 78.8 8.2 compost  8 b 3  15 b 10 54 ab 81 174 66 1.6 1.1 71.1 5.5 mulch  16 ab 8  27 ab 32 17 b 40 288 173 1.5 1.3 73.4 10.3  ANOVA Resultsb df P F  P F P F P F P F P F  Amendment 2 0.048 7.1  0.033 5.1 0.048 4.3 0.2 1.5 0.5 0.7 0.1 2.8  Block 2 0.3 1.6  0.05 4.2 0.7 0.2 0.011 7.8 0.06 3.6 0.08 5.1  Amendment* Block 4 0.4 1.1  0.3 1.3 0.8 0.3 0.7 0.5 0.4 1.01 0.7 0.5 a = Soil amendments sharing the same letter within a column do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level. c = Pratylenchus spp. 50 g-1 soil at harvest divided by Pratylenchus spp. 50 g-1 soil at time of planting.   101   Figure 3.2 a) The population density of log of Pratylenchus spp. g-1 dry root +1 was positively associated with square-root of % necrotic root surface area (n=6 for each treatment) (Pearson correlation; r=0.53, P=0.012) at Site 2. The equation of the line for the regression is y = 8.4 + 0.28x (df=17; F=6.2; P=0.024; R2=0.28). b) The population density of log of Pratylenchus spp. g-1 dry root +1 was negatively associated with total root surface area (n=6 for each treatment) (Pearson correlation; r= -0.47; P=0.023) at Site 2. The equation of the line for the regression is y = 34.5 – 4.0x (df=17; F=4.7; P=0.046; R2=0.22). Black circles represent non-amended (bare), blue circles represent compost-amended, and red circles represent mulch-amended soil treatments.  3.3.3.3 Site 3             Plants generally grew larger in mulch and non-amended (bare) soil than those in the compost-amended soil. Plant growth was greater in soil with lower microbial activity (sterilized) than in untreated (non-sterilized) soil for the variables shoot height increment and total root length (Table 3.14). Effects of sterilization on root surface area and root weight depended on how the soil had been treated in the field: in non-amended soils only, root weight was greater with lower microbial activity than in untreated soil.   The populations of Pratylenchus spp. in soil and roots of plants were lower in compost than in non-amended soil (Table 3.15). The population of Pratylenchus spp. in roots did not   102 correlate with percent root necrosis (Pearson correlation; r=0.1; P=0.6); however, Pratylenchus spp. g-1 root was negatively correlated with total root length (Pearson correlation; r= -0.41; P=0.04) (Figure 3.3 A, B).        103 Table 3.14 Effect of soil amendment (bare, compost, or mulch) and/ or sterilization regime (sterilized or non-sterilized) on measures of plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height  increment) for plants grown in soil from Site 3. Values represent means and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.  Plant growth measurements  Treatment total root length (cm) SD total surface area (cm2) SD root weight (g) SD shoot weight (g) SD plant weight (g) SD shoot height  increment (cm) SD Amendment Effects a             bare (n=12) 987 28 35.8  a 11.6 0.89 a 0.36 1.0 a 0.2 2.9 3.8 10.9 4.8 compost (n=12) 925 140 24.5 b 10.6 0.38 b 0.21 0.7 b  0.2 0.9 0.4 10.5 1.9   mulch (n=12) 967 104 31.1 ab 11.5 0.72 a 0.23 1.0 a 0.1 1.5 0.3 12.1  2.1 Sterilization Effectsb                 non-sterilized (n=18) 929 b 138 29.0 14.6 0.6 0.27 0.81 0.32 1.5 0.4 10.0 b 3.1 sterilized (n=18) 990 a 27 32.1 8.6 0.7 0.41     0.80 0.24 2.2 3.3 12.1 a 3.3 Amendment * Sterilization Effectsc            bare + non-sterilized (n=6) 966   20 30.2 ab 9.1 0.64 bc 0.26 0.85  0.12 1.5 0.3 10.1 4.6 bare + sterilized (n=6) 1007  18 41.53 a 11.7 1.2 a  0.25 1.1  0.2 4.3 5.2 11.7 5.2 compost + non-sterilized (n=6) 996   20 28.2 ab 9.5 0.45 c 0.23 0.63  0.30 1.1 0.5 11.5 2.0   compost + sterilized (n=6) 853  176 20.8 b 11.5 0.32 c 0.21 0.74  0.051 0.82 0.45  9.6 1.5 mulch + non-sterilized (n=6) 1007  23 37.3 ab 5.1 0.89 ab 0.17 0.97  0.14 1.8 0.2  13.3 2.5 mulch + sterilized (n=6) 927   140 24.7 ab 13.1 0.56 bc 0.18 0.84  0.090 1.3 0.3 10.8 0.5 ANOVA Resultsd P F P F P F P F P F P F Amendment (df=2) 0.07 2.9 0.04 3.8 <0.001 13 0.003 8.3 0.08 2.8 0.6 0.5 Sterilization (df=1) 0.005 10.0 0.3 0.7 0.7 0.07 0.2 1.7 0.4 0.9 0.04 4.9 Block (df=2) 0.002 8.9 0.04 3.6 0.2 1.8 0.3 1.0 0.2 1.3 0.3 1.1 Amendment * Sterilization (df=2) 0.052 3.4 0.008 6.3 0.001 9.7 0.051 3.4 0.1 2.3 0.2 1.1 Amendment * Block (df=4) 0.1 2.1 0.4 0.9 0.8 0.3 0.6 0.5 0.3 1.2 0.3 1.2 Sterilization * Block (df=2) 0.01 5.5 0.5 0.6 0.9 0.09 0.4 0.9 0.3 1.02 0.9 0.03 Amendment * Sterilization * Block (df=4) 0.1 1.9 0.8 0.3 0.7 0.4 0.9 0.2 0.4 1 0.9 0.1 a = Soil amendment effects not sharing the same letter within a column differ significantly (P>0.05), according to Tukey’s HSD test. b = Soil sterilization effects not sharing the same letter within a column differ significantly (P>0.05). c = Soil amendment by sterilization effects not sharing the same letter within a column differ significantly (P>0.05), according to Tukey’s HSD test. d = Results are significant at a P≤0.05 significance level.        104 Table 3.15 Effect of soil amendment (bare, compost, or mulch) on population densities of Pratylenchus spp. in soil at time of planting, and Pratylenchus spp. in soil and roots, total nematodes in soil, multiplication rate of Pratylenchus spp., and the percent necrotic root surface area at harvest at Site 3. Values represent means (n=6 for each treatment) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.   At planting   At harvest          Amend-menta   Pratylenchus spp. 50 g-1  soil SD  Pratylenchus  spp. 50 g-1  soil SD Pratylenchus spp. 1 g-1 dry root SD Total Nematodes 50 g-1 soil SD Multiplication Rate of Pratylenchus spp. 50 g-1  soilc SD % Necrotic root surface area SD bare  26 5  50 a 23 182 a 212 230 118   2.0 0.6 81.2 3.5 compost  9 7  15 b 9 14 b 22 302 150     2.3 1.9 79.4 5.7 mulch  13 5  34 a 21 31 ab 58 266 91 2.8 2.4 80.9 4.3  ANOVA Resultsb df P F  P F P F P F P F P F  Amendment 2 0.034 8.0  0.002 14.1 0.032 5.1 0.6 0.4 0.7 0.2 0.8 0.1  Block 2 0.5 0.8  0.1 2.1 0.8 0.1 0.1 2.1 0.8 0.1 0.9 0.017  Amendment * Block 4 0.3 1.3  0.1 2.3 0.7 0.4 0.4 0.9 0.2 1.4 0.1 2.1 a = Soil amendments sharing the same letter within a column do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level. c = Pratylenchus spp. 50 g-1 soil at harvest divided by Pratylenchus spp. 50 g-1 soil at time of planting.  105   Figure 3.3 a) The population density of log of (Pratylenchus spp. g-1 dry root + 1) was not associated with the square root of % necrotic root surface area (n=6 for each treatment) (Pearson correlation; r= -0.31; P=0.3) at Site 3. The equation of the line for the regression was y = 9.01 - 0.033x (df=17; F=0.28; P=0.6; R2=0.1). b) The population density of log of Pratylenchus spp. g-1 dry root + 1 was negatively associated with total root length (n=6 for each treatment) (Pearson correlation; r= -0.41; P=0.04) at Site 3. The equation of the line for the regression was y = 35.9 – 3.5x (df=17; F=3.2; P=0.09; R2=0.2). Black circles represent non-amended (bare), gray circles represent compost-amended, and red circles represent mulch-amended soil treatments.    3.3.4 Site 4  3.3.4.1 Field experiment: Effect of soil treatments on soil biotic and abiotic properties  Site 4 is an old, well-established orchard that traditionally produced sweet cherry, and it was replanted with sweet cherry in spring of 2015. For most variables, the effect of fumigation depended on sampling year (Tables 3.16, 3.17, 318). Only permanganate oxidizable carbon (POXC), and Pratylenchus abundance g-1 dry root were higher in non-fumigated soil than in fumigated soil across both years (Table 3.17 and Table 3.18).  Cation exchange capacity (CEC), Ca, and K were higher in non-fumigated soil relative to those in fumigated soil in 2016 (Table 3.16). Microbial activity, as measured by FDA hydrolysis, and bacterial and fungal abundance   106 had the same trend, but only in 2015 (Table 3.18).   Cation exchange capacity (CEC), Ca, K, Mg, POXC, TN, TC, OC, OM, pH, and 18S fungal abundance were all higher in the compost-amended soil than in non-amended soil (Table 3.16 and Table 3.17). The effect of compost on TN and TC was stronger in 2016 than 2015 (Table 3.17). The opposite was true for fungal abundance and F: B ratio, which were greater in the compost-amended soil than in non-amended soil in 2015 (Table 3.18). Pratylenchus abundance g-1 dry root was lower in compost-amended soil than in non-amended soil across years (Table 3.18). There was higher soil P in the legacy P-fertigated plots, and higher OC and OM in legacy mulch plots, relative to those in the untreated plots (Tables 3.16 and Table 3.17). In addition, there was greater POXC in legacy mulch plots than in that of P-fertigated plots (Table 3.17). All three-way and four-way interactions involving fixed main effects are in Appendix B (Tables B.3.17-B.3.19).                107  Table 3.16 Main effects, and one-way and two-way interactions of the effect of soil treatment (fumigation, compost, or legacy effects) on cation exchange capacity (CEC), electrical conductivity (EC), and nutrients phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) at Site 4 in October 2015 and 2016 sampling years. Values represent main factor means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Treatmenta CEC (meq 100 g-1 soil) SD P  (mg  kg-1) SD Ca (mg  kg-1) SD K (mg  kg-1) SD Mg  (mg  kg-1) SD Na  (mg  kg-1) SD EC (uS cm-1) SD Overall  Fumigation Effects (n=72)                 Fumigation  8.9 1.6 97 40 1321 234 233 78 163 27 43  13 0.1 0.7   No Fumigation  9.4 1.7 91 62 1370 247 241 70 172 39 33  7 0.1  0.5  Compost Effects (n=72)                 Compost  9.5 a 1.6 97 57 1389 a 235 261 a 63 181 a 34 37 9 0.1 0.6   No Compost  8.8 b 1.6 91 48 1302 b 243 213 b 78 155 b 29 39 13 0.1 0.88   Legacy Effects (n=48)                 Mulch 9.5 1.7 84 b 45 1370 263 250 82 170 35 39 11 0.1 0.8   No treatment 8.9 1.6 84 b 49 1319 221 230 71 164 31 36 13 0.1 0.8   P-fertigation 9.2 1.7 114 a 58 1348 246 231 69  169 37 39 11 0.1 0.6 2015 Fumigation Effects (n=36)                 Fumigation  8.9 1.3 117                         53 1250 189 260 88  162 23 31 6 0.1 0.5   No Fumigation  9.1 1.5 114 94 1231 194 254 68     166 30 26 3 0.1 0.8  Compost Effects (n=36)                 Compost  9.2 1.5 121 85 1259 190 282 69 174 25 27 4 0.8 0.4   No Compost  8.8 1.4 109 67 1222 192 232 81 154 25 30 6 0.1 0.9   108 Year Treatmenta CEC (meq 100 g-1 soil) SD P  (mg  kg-1) SD Ca (mg  kg-1) SD K (mg  kg-1) SD Mg  (mg  kg-1) SD Na  (mg  kg-1) SD EC (uS cm-1) SD  2015 Legacy Effects (n=24)                 Mulch 9.2 1.3 106 64 1245 208 265 87 162 25 29.6 5.0 103 66   No treatment 8.7 1.5 104 73 1225 179 256 79 161 26 27.3 5.7 92 45   P-fertigation 9.0 1.5 137 88 1252 190 250 71 168 30 30.0 6.9 109 95  2016 Fumigation Effects (n=36)                 Fumigation  8.9 b 1.7 77 27 1391 b 278 205 b 68 164 31 53.9 a 20.5 167 a 108   No Fumigation  9.7 a 1.9 67 30 1509 a 300 228 a 71 178 48 39.6 b 11.7 103 b 30  Compost Effects (n=36)                Compost  9.8 a 1.7 73 29 1519 a 279 239 57 187 a 42 45.2 14.9 137 82   No Compost  8.8 b 1.8 72 30 1381 b 294 193 75 155 b 34 48.3 20.9 133 89 2016 Legacy Effects (n=24)                 Mulch 9.7 2.0 62 26 1494 318 235 78 178 44 48.5 17.2 149 104   No treatment 9.1 1.7 64 26 1413 263 203 63 166 36 44.6 21.4 132 95   P- fertigation 9.3 1.9 91  28 1443 303 212 67 169 44 47.2 15.7 123 43  ANOVA Resultsb, c P F P F P F P F P F P F P F  Fumigation (df=1) 0.06 3.7 0.4 0.6 0.1 2.5 0.5 0.5 0.5 0.5 0.1 2.7 0.4 0.6  Compost (df=1) 0.01 7.1 0.5 0.3 0.03 5.1 <0.001 16 <0.001 16 0.06 3.7 0.5 0.3  Legacy (df=2) 0.2 1.5 0.02 5.4 0.5 0.5 0.3 1.3 0.3 1.3 0.7 0.4 0.5 0.5  Block (df=5) <0.001 15.2 0.001 5.2 <0.001 13.5 <0.001 12.9 <0.001 12 <0.001 8.5 0.005 4.1  Fumigation * Compost (df=1) 0.5 0.3 0.54 0.4 0.5 0.3 0.13 1.8 0.7 0.12 0.8 0.05 0.5 0.4   109 ANOVA Resultsb, c P F P F P F P F P F P F P F  Fumigation * Legacy (df=2) 0.9 0.06 0.8 0.1 0.5 0.6 0.5 0.6 0.2 1.5 0.9 0.08 0.8 0.15  Fumigation * Block (df=5) 0.02 2.9 <0.001 5.7 0.003 4.1 0.13 1.8 <0.001 5.4 0.2 1.7 0.7 0.12  Compost * Legacy (df=2) 0.8 0.1 0.9 0.06 0.28 1.3 0.2 1.5 0.2 1.5 0.9 0.08 0.2 1.5  Compost * Block (df=5) 0.2 1.3 0.1 1.7 0.067 2.3 0.09 2.1 0.8 0.4 0.2 1.6 0.03 2.6  Legacy * Block (df=10) 0.06 2.1 0.9 0.25 0.6 0.8 0.6 0.9 0.2 1.4 0.5 0.9 0.5 0.8  Year (df=1) 0.03 6.4 <0.001 34.9 <0.001 71.6 <0.001 55 0.026 5.3 <0.001 91.4 0.009 7.7  Year * Fumigation (df=1) 0.04 4.2 0.5 0.41 0.003 9.4 0.02 5.6 0.1 2.6 0.005 8.4 0.01 6.9  Year * Compost (df=1) 0.01 6.8 0.4 0.58 0.049 4.2 0.7 0.1 0.05 4 0.05 4 0.5 0.3  Year * Legacy (df=2) 0.8 0.15 0.9 0.06 0.5 0.63 0.2 1.5 0.2 1.5 0.2 1.8 0.2 1.5  Year * Block (df=5) 0.001 5.5 0.02 2.95 <0.001 6.3 0.09 2.1 0.002 4.9 0.001 5.2 0.03 2.8 a = Fumigation effects, compost or legacy effect means not sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level. c = All 3-way and 4-way interactions listed in Table B.3.17 (Appendix B).                   110 Table 3.17 Main effects, and one-way and two-way interactions of the effect of soil treatment (bare, compost, or mulch) on permanganate oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio and pH at Site 4 in October 2015 and 2016 sampling years. Values represent main factor means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.  Year Treatmenta mg POXC kg-1 soil SD TN (%) SD TC  (%) SD OC  (%) SD OM  (%) SD C: N SD pH SD Overall  Fumigation Effects (n=72)                 Fumigation  449 b 135 0.09 0.01 1.3 0.2 1.2 0.3 2.5 0.5 14.3 2.6 7.1 0.2   No Fumigation  516 a 176 0.09 0.01 1.4 0.4 1.3 0.5 2.7 0.8 15.2 3.4 7.2 0.2   Compost Effects (n=72)                 Compost  546 a 117 0.1 a 0.02 1.4 a 0.3 1.5 a 0.4 2.9 a 0.7 15.1 3.3 7.5 a  0.2   No Compost  419 b 106 0.08 b 0.01 1.3 b 0.2 1.1 b 0.3 2.2 b 0.6 14.4 2.8 7.2 b 0.2   Legacy Effects (n=48)                 Mulch 505 a 159 0.09 0.02 1.4 0.4 1.6 a 0.5 2.9 a 0.5 15.8 3.9 7.2 0.1   No treatment 486 ab 135 0.09 0.02 1.3 0.2 1.0 b 0.2 2.1 b 0.5 14.1 2.6 7.3 0.2   P-fertigation 387 b 111 0.08 0.02 1.0 0.3 1.0 b 0.3 2.7 a 0.6 13.4 1.0 7.3 0.2                 2015 Fumigation Effects (n=36)                 Fumigation  507  165 0.1 0.01 1.5 0.2 1.4 0.3 2.3 0.5 14.7 3.7 7.0 0.2   No Fumigation  592   228 0.1 0.01 1.6 0.5 1.5 0.6 2.5 1.1 15.8 5.2 7.0 0.2  Compost Effects (n=36)                 Compost  612 a  128 0.1 0.02 1.6 0.5 1.6 0.5 2.6 0.9 15.5 5.2 7.1 0.1   No Compost  488 b 122 0.1 0.006 1.6 0.2 1.3 0.4 2.2 0.6 14.9 3.8 7.0 0.2   111 Year Treatmenta mg POXC kg-1 soil SD TN (%) SD TC  (%) SD OC  (%) SD OM  (%) SD C: N SD pH SD 2015  Legacy Effects (n=24)                 Mulch 528 230 0.12 .01 1.5 0.2 1.6 0.4 2.6 0.6 15.5 2.2 6.9 0.1   No treatment 619 200 0.1 0.02 1.6 0.6 1.4 0.7 2.4 1.2 15.7 6.5 7.1 0.1   P-fertigation 504 160 0.1 0.02 1.5 0.2 1.4 0.2 2.2 0.4 14.5 4.1 7 0.2 2016 Fumigation Effects (n=36)                 Fumigation  390  106 0.07 0.02 1.0 0.2 0.98 0.24 2.6 0.6 13.9 1.5 7.2 0.1   No Fumigation  440   125 0.07 0.02 1.1 0.3 1.1 0.3 2.9 0.7 14.6 1.6 7.3 0.2   Compost Effects (n=36)                 Compost  480  106 0.08 a 0.01 1.2 a 0.2 1.2 0.2 2.9 0.6 14.6 1.4 7.3 0.2    No Compost  350  91 0.07 b 0.01 0.9 b 0.2 0.87 0.32 2.5 0.6 13.9 1.7 7.3 0.2 2016 Legacy Effects (n=24)                 Mulch 390 118 0.07 0.02 1.2 0.2 1.2 0.2 3.0 0.8 15.8 a 1.4 7.2 0.1   No treatment 468 110 0.07 0.01 0.9 0.2 0.94 0.25 2.5 0.5 13.6 b 1.2 7.5 0.1   P-fertigation 387 111 0.07 0.02 1.0 0.3 0.97 0.30 2.7 0.6 13.4 b 1.0 7.3 0.2 ANOVA Resultsb, c P F P F P F P F P F P F P F  Fumigation (df=1) <0.001 12 0.15 2.1 0.1 2.2 0.1 2.2 0.09 2.9 0.2 1.3 0.3 1.4  Compost (df=1) <0.001 12 <0.001 15 0.003 10.2 <0.001 24 <0.001 15 0.4 0.5 0.016 12.6  Legacy (df=2) 0.026 5.4 0.32 1.2 0.1 1.9 0.014 4.9 0.033 3.7 0.1 2.3 0.1 25  Block (df=5) 0.002 4.8 0.06 2.4 0.06 2.4 <0.001 9.1 <0.001 11 0.4 1 0.39 0.9  Fumigation * Compost (df=1) 0.6 0.1 0.1 2.1 0.7 0.1 0.7 0.1 0.9 0.001 0.5 0.3 0.6 0.4   112  ANOVA Resultsb, c P F P F P F P F P F P F P F  Fumigation * Legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.009 7.8  Fumigation * Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.4 1.0 0.6 0.6 0.5 0.8 0.5 0.8  Compost * Legacy (df=2) 0.1 1.9 0.06 2.9 0.7 0.3 0.7 0.3 0.5 0.5 0.1 2.2 0.1 2.5  Compost * Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.2 1.4 0.5 0.8 0.5 0.8  Legacy * Block (df=10) 0.6 0.7 0.9 0.4 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2  Year (df=1) <0.001 34 <0.001 145 <0.001 83.8 0.026 73.1 <0.001 18 <0.001 5.4 <0.001 158  Year * Fumigation (df=1) 0.3 0.7 0.8 0.03 0.8 0.06 0.8 0.06 0.8 0.05 0.8 0.02 0.8 0.02  Year * Compost (df=1) 0.003 10.1 0.003 10 <0.001 0.7 0.7 0.1 0.9 0.001 0.5 0.3 0.6 0.3  Year * Legacy (df=2) 0.1 1.9 0.06 2.9 0.7 0.3 0.7 0.3 0.5 0.5 0.1 2.2 0.1 2.5  Year * Block (df=5) <0.001 6.2 <0.001 6.2 0.1 1.6 0.1 1.6 0.6 0.6 0.5 0.8 0.5 0.8 a = Fumigation effects, compost or legacy effect means not sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level. C = All 3-way and 4-way interactions listed in Table B.3.18 (Appendix B).                 113 Table 3.18 Main effects, and one-way and two-way interactions of the effect of soil treatment (bare, compost, or mulch) on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S copy number) and total fungi (18S copy number), fungal-to-bacterial (F: B) ratio, % root colonization by arbuscular mycorrhizal fungi (AMF), and Pratylenchus spp. per one-gram root and 50-grams soil at Site 4 in October 2015 and 2016 sampling years. Values represent main factor means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Treatmenta FDA hydro-lysis (µg g-1) SD Log (16S copy # g-1 dry soil) SD Log (18S copy # g-1 dry soil) SD F: B ratio SD % AMF Coloniz-ation SD Praty- lenchus spp. 100 g-1 soild SD Praty- lenchus spp. g-1 dry rootd SD Overall  Fumigation Effects (n=72)                 Fumigation  1.8 1.4 8.8 0.4 7.2  0.50 0.82 0.093 34.9 17.60 50 75.3 130 b 252.6   No Fumigation  2.6 1.7 8.9 0.5 7.5  0.56 0.84 0.083 36.8 16.95 52 42.6 381 a 408.0  Compost Effects (n=72)                 Compost  2.4 1.8 8.9 0.5 7.4 0.52 0.84 0.071 37.3 17.25 47 74.9 156 b 226.7   No Compost  2.1 1.5 8.8 0.6 7.3 0.58 0.83 0.097 34.1 16.85 50 49.7 354 a 442.5  Legacy Effects (n=48)                 Mulch                 No treatment 2.0 1.6 9.0 0.3 7.4 0.64 0.82 0.068 36.8 17.60 49 62.5 204 270.5   P- fertigation 2.5 1.7 8.7 0.7 7.3 0.54 0.84 0.107 35.8 16.95 49 81.7 303 449.2 2015 Fumigation Effects (n=36)                 Fumigation  1.9 b 1.4 9.0 b 0.3 7.6 b 0.584 0.85 0.0748 49.1 17.9 2  4.7 139 305.2   No Fumigation  2.5 a 2.4 9.4 a 0.3 8.1 a 0.365 0.86 0.0485 51 17.9 10  1.1 313 379.1  Compost Effects (n=36)                 Compost  2.4 2.3 9.2 0.6 8.1 a 0.487 0.88 a 0.0694 54.2 18 2 3.6 150 197.5   No Compost  2.1 1.7 9.1 0.455 7.7 b 0.522 0.84 b 0.0515 45.2 17 2 15.6 301 449.6   114 Year Treatmenta FDA hydro-lysis (µg g-1) SD Log (16S copy # g-1 dry soil) SD Log (18S copy # g-1 dry soil) SD F: B ratio SD % AMF Coloniz-ation SD Praty- lenchus spp. 100 g-1 soild SD Praty- lenchus spp. g-1 dry rootd SD 2015 Legacy Effects (n=24)                 Mulch 1.8 2.2 9.3 0.4 7.9 0.5 0.85 0.062 52.4 16.9 6 12 176 245   No treatment 2.8 2.1 9.1 0.6 7.8 0.5 0.86 0.070 47.9 17.5 5 10 303 508   P-fertigation 2.11 1.8 9.2 0.4 7.9 0.5 0.86 0.057 49.9 19.3 6 12 197 237 2016 Fumigation Effects (n=36)                 Fumigation  1.7 1.4 8.5 0.5 6.7 0.4 0.79 0.11 20.6 17.3 97 145 120 199   No Fumigation  2.6 1.1 8.3 0.6 6.8 0.7 0.82 0.11 22.5 16.0 93 84 448 436   Compost Effects (n=36)                 Compost  2.4 1.3 8.5 0.4 6.6 0.5 0.79 0.073 20.3 16.5 92 146 161 255   No Compost  2 1.3 8.4 0.7 6.8 0.6 0.82 0.14 22.9 16.7 98 83 407 435  Legacy Effects (n=24)                Mulch 2.2 1.1 8.6 0.2 6.8 0.7 0.78 0.073 21.1 18.3 92 112 232 295   No treatment 2.2 1.3 8.2 0.8 6.7 0.5 0.82 0.14 23.7 16.4 92 152 303 390   P-fertigation 2.1 1.7 8.4 0.6 6.7 0.5 0.81 0.11 19.9 15.4 101 84 318 437  ANOVA Resultsb P F P F P F P F P F P F P F  Fumigation (df=1) 0.5 0.3 0.3 0.8 0.05 0.09 0.2 1.4 0.2 1.5 0.06 3.7 <0.001 14.7 Compost (df=1) 0.8 0.03 0.1 1.7 <0.001 17 0.8 0.06 0.09 2.0 0.06 4.0 0.007 7.8  Legacy (df=2) 0.9 0.06 0.8 0.4 0.9 0.06 0.5 0.5 0.1 2.2 0.5 0.6 0.9 0.06 Block (df=5) 0.1 1.7 0.2 1.1 <0.001 6.031 0.1 1.9 0.2 1.2 0.002 4.7 0.09 1.9   115  ANOVA Resultsb P F P F P F P F P F P F P F  Fumigation * Compost (df=1) 0.2 1.5 0.8 0.06 <0.001 6.0 0.2 1.4 0.6 0.2 0.2 1.6 0.2 1.6  Fumigation * Legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.009 7.8  Fumigation * Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.4 1.03 0.6 0.6 0.5 0.8 0.5 0.8  Compost * Legacy (df=2) 0.1 1.9 0.06 2.9 <0.001 4.0 0.7 0.3 0.5 0.5 0.1 2.2 0.1 2.5  Compost * Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.2 1.4 0.5 0.8 0.5 0.8  Legacy * Block (df=10) 0.6 0.7 0.9 0.4 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2  Year (df=1) 0.8 0.02 <0.001 52 <0.001 274 <0.001 12 <0.001 71 <0.001 245 0.3 1.1  Year * Fumigation (df=1) <0.001 6.3 0.008 7.4 <0.001 10 0.7 0.1 0.6 0.2 0.2 1.3 0.1 2.3  Year * Compost (df=1) 0.2 1.5 0.8 0.06 <0.001 13 <0.001 6.1 0.7 0.07 0.8 0.02 0.2 1.6  Year * Legacy (df=2) 0.9 0.06 0.8 0.4 0.8 0.4 0.7 0.3 0.1 2.2 0.5 0.59 0.1 2.2  Year * Block (df=5) 0.01 3.5 0.2 1.1 <0.001 21 <0.001 6.6 0.3 1.0 0.01 3.3 0.407 1.044 a = Fumigation effects, compost or legacy effect means not sharing the same letter within a column under the same sampling year do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level. c = All 3-way and 4-way interactions listed in Table B.3.19 (Appendix B). d = Data courtesy of Dr. Thomas Forge in October 2016 sampling year.        116 3.3.4.2 Greenhouse experiment: Effect of soil treatments on plant growth and  Pratylenchus spp. abundance         Reduction of microbial of activity in soil by means of sterilization resulted in lower plant weight and root surface area than those of untreated (non-sterilized) soil (Table 3.19).  Similarly, root surface area and plant weight were lower when plants were grown in fumigated field soil than in traditionally non-fumigated field soil. When plants were grown in compost-amended soil root length was greater than that of plants grown in non-amended soil.          Neither the field treatments nor lab sterilization affected Pratylenchus abundance in roots or soil (Table 3.20). Surprisingly, percent necrotic root surface area was greater in the fumigated than in the non-fumigated soil treatment (Table 3.20). There was no correlation between Pratylenchus spp. g-1 root and percent necrotic root surface area (Pearson correlation; r= 0.034; P=0.7), or total root surface area (Pearson correlation; r= - 0.42; P=0.7). There were no significant two-way, three-way, four-way, or five-way interactions involving fixed main effects for plant growth data or nematode abundance data (Figure B.3.20).  117  Table 3.19 Effect of soil treatment (fumigation effects, compost effects, or legacy effects) and/ or sterilization regime (sterilized or non-sterilized) on measures of plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height increment) for plants grown in soil from Site 4. Values represent main factor means and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given.  Plant growth measurements Treatmenta root length (cm) SD root surface area (cm2) SD root weight (g) SD shoot weight (g) SD plant weight (g) SD shoot height  increment (cm) SD Fumigation Effects (n=72)             Fumigation  968 34 44.4 b 21.2 0.66 b 0.38 0.64 b 0.38 1.2 b 0.6 10.9 2.4 No Fumigation  977  35 54.4 a 22.0 0.96 a 0.39 0.82 a 0.40 1.9 a 0.6 11.3 2.5 Compost Effects (n=72)             Compost  987 a 34 46.5 21.6 0.76 0.39 0.69 0.39 1.4 0.6 11.1 2.5 No Compost  940 b 34 50.5 21.6 0.79 0.39 0.75 0.39 1.6 0.6 11.4 2.5 Legacy Effects (n=48)             Mulch 945 34 40.9 21.6 0.64 0.38 0.66 0.38 1.3 0.7 11.1 3.1 No treatment 986 35 52.8 21.6 0.85 0.38 0.74 0.38 1.6 0.7 11.2 3.1 Phosphorus fertigation 959 34 51.7 21.6 0.84 0.38 0.76 0.38 1.6 0.7 11.4 3.1 Sterilization Effects (n=72)             Sterilization 991 37 56.5 a 23.2 1.1 a 0.41 0.79 a 0.42 1.9 a 0.6 11.1 2.7 No Sterilization 941 32 41.8 b 20.3 0.57 b 0.36 0.66 b 0.36 1.2 b 0.5 11.5 2.3 ANOVA Resultsb, c P F P F P F P F P F P F  fumigation (df=1) 0.2 1.5 0.015 6.1 <0.001 16 0.003 9.2 <0.001 12.9 0.2 1.5  compost (df=1) 0.001 50 0.8 0.06 0.2 1.6 0.6 0.2 0.8 0.06 0.2 1.4  legacy (df=2) 0.8 0.1 0.1 1.9 0.1 2.2 0.5 0.5 0.06 2.9 0.7 0.3  sterilization (df=1) 0.5 0.3 <0.001 16.3 <0.001 4.3 0.003 9.5 <0.001 33.6 0.2 1.5  block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.7 0.5 0.2 1.4 a = Results are significant at a P≤0.05 significance level. b = Values are significant at a P ≤ 0.05 significance level. c = There were no significant 2-way, 3-way, or 4-way interactions observed.      118 Table 3.20 Effect of soil treatment (fumigation effects, compost effects, or legacy effects) on abundance of Pratylenchus spp. in soil at time of planting, and Pratylenchus spp. in soil and roots, total nematodes in soil, multiplication rate of Pratylenchus spp., and the percent necrotic root surface area at harvest at Site 4. Values represent main factor means and standard deviations (SD). Degrees of freedom (df), and P-and F-values for between- and within- subject factors for each variable from the ANOVA are given.   At planting   At harvest         Treatmenta Pratylenchus spp. 100 g-1 soil SD Pratylenchus spp. 100 g-1 soil SD Pratylenchus spp. 1 g-1 dry root SD Total Nematodes 50 g-1 soil SD Multiplication Rate of Pratylenchus spp. 100 g-1 soil SD %Necrotic root surface area SD Fumigation Effects (n=72)             Fumigation  97 145 54 37 454 963 187 161 3.9 5.7 78.6 a 6.8 No Fumigation  93 8 60 29 400 671 179 154 3.5 4.4 70.6 b 5.7 Compost Effects (n=72)             Compost  92 146 60 38 386 854 194 193 4.2 5.8 76.9 6.6 No Compost  98 83 55 28 468 805 172 111 3.2 4.3 77.6 6.2 Legacy Effects (n=48)            Mulch 93 112 56 38 540 105 173 95 2.9 2.7 76.3 6.6 No treatment 92 152 52 36 385 847 203 186 3.0 2.9 76.5 5.5 Phosphorus fertigation 101 84 64 24 357 511 174 179 5.2 7.8 79.1 6.8 ANOVA  Resultsb, c P F P F P F P F P F P F Fumigation (df=1) 0.2 1.5 0.5 0.3 0.3 0.8 0.5 0.4 0.3 0.9 0.007 19.9 Compost (df=1) 0.2 1.5 0.8 0.06 0.2 1.5 0.8 0.06 0.2 1.5 0.8 0.06  Mulch (df=5) 0.7 0.5 0.7 0.5 0.4 0.9 0.1 1.6 0.4 1.0 0.6 0.6  Block (df=5) 0.1 1.6 0.04 2.5 0.002 4.7 0.7 0.5 0.4 0.9 0.1 1.6 a = Soil treatments not sharing the same letter within a column differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level. c = There were no significant 2-way, 3-way, or 4-way interactions observed.       119 3.4 Discussion 3.4.1 Effect of the soil amendments on soil biotic and abiotic properties among soils from  Sites 1, 2, and 3 3.4.1.1 Field experiments           The effect of compost and mulch application on soil physical, chemical, and biological properties was assessed through two growing seasons at two newly-established cherry orchards, and one old orchard, that had been replanted to cherry in 2013. Several studies have demonstrated the benefits of organic amendments with regard to physical, chemical, and biological soil properties (Ferreras et al. 2006; Tejada et al. 2009; Torres et al. 2015). Our results agree with these findings, as the compost treatment increased soil phosphorus, POXC, total nitrogen, total carbon, organic carbon, and organic matter compared to woodchip mulched or bare plots. This effect was more noticeable in newly-cultivated orchard soils (Sites 1 and 2), than in established orchard soil (Site 3). Other studies have shown that the quantity and quality of soil organic carbon inputs affect the activity of decomposer organisms within the soil food web, which, in turn, influences nutrient cycling, biological suppressiveness, and crop growth (Hoitink and Boehm 1999; Westphal and Becker 1999).  There were few effects of the amendments on soil biological properties at Sites 1, 2, and 3. Although there were differences in means among soil amendment treatments for some of the biological data, there were no significant differences for most variables due to the very high variability of the data. Amendments were surface applied for two consecutive years in this study, and after this short amount of time the amendments may have influenced soil biological properties in only the top few centimeters of soil (Yang et al. 2003). Soil samples were taken from a 0-30 cm depth profile, which may have diluted any effects that were present. The method   120 of soil sampling is important as soil biological properties vary at a very fine scale (Yang et al. 2003). It can be concluded that the spatial and temporal variability of microbial abundance and activity in the experimental plots at Sites 1, 2, and 3 was very high, and the effect of the soil amendments was not quite strong enough after two years to produce significant differences. At Site 3, in particular, any effects of the amendments on soil biology may have been disguised by the orchard heterogeneity that existed. The experimental plots in this orchard were spatially segregated over a large area, which may have been related to the block effects observed for some biotic variables, including: FDA hydrolysis, 18S fungal abundance, and Pratylenchus g-1 root. Furthermore, the trees in some blocks performed so poorly that the orchard manager removed the immature fruit from those areas to reduce stress on the trees.  In this study, amendment application did not affect Pratylenchus abundance in soil and roots. Past studies have shown that abiotic properties of soil, such as total nitrogen, total carbon, and organic carbon, are related to suppression of Pratylenchus (Stirling et al. 2003) and other soil pathogens (Janvier et al. 2007; Bonilla et al. 2015). However, in the current study, the increase of certain soil nutrients (i.e. organic carbon, total nitrogen, and total carbon) in compost- amended soil did not coincide with a lower abundance of Pratylenchus in roots or soil. One consistent result in this study was that at Sites 1, 2, and 3 the percent AMF root colonization was lower in compost- and mulch- amended soil, relative to non-amended soil. In this study, compost was applied generously, at a rate of 0.05 m3/ m2, for two consecutive years, and there was an increase in total P and N in compost-amended soils at all three sites. Although organic sources of nutrients, such as composts, have been shown to promote AMF root colonization (Meyer et al. 2015), the excessive use of organic amendments, which are a source of P and N, can result in a decrease in AMF colonization (Jordan et al. 2000; Cavagnaro 2014).   121 However, this does not explain the decreased AMF colonization in mulch-amended soil, as the P and N contents were not different between the bare and mulch plots. One possible explanation is that organic amendments, such as woodchip mulch, cause increased root growth, as was observed by Kumar et al. (2011) and Jindo et al. (2012). Since percent root colonization depends on both the standing root length of the plant and AMF abundance, changes in percent root length could have resulted from changes in standing root length, and may not have been related to AMF abundance (Treseder 2013). The standing root length of plants can change depending on developmental stages (Troughton 1956; Bartelink 1998), season (Hendrick and Pregitzer 1996), soil moisture (Schenk and Jackson 2002), and nutrient availability (Reynolds and Dantonio 1996). Although root growth was not directly measured in the field experiments, upon qualitative observation during root sampling each year, fine root growth appeared to be greater in compost- and mulch- amended plots than in the bare plots. In the greenhouse experiment, many root growth parameters were measured (i.e. root weight, root length, and root surface area), and at Site 1 root surface area and root weight were greater in amended soil than non-amended soil, and at Site 2, root length was greater in amended soil than non-amended soil.  3.4.1.2 Greenhouse experiments  Although the amendments did not cause a reduction in Pratylenchus abundance in soil and roots in the field experiments, effects of these treatments were observed in the greenhouse bioassay. In soil from Sites 1 and 2, Pratylenchus abundance in roots was lower and root surface area was greater when plants were grown in compost-amended soils than in the non-amended control. In addition, there was evidence that healthier roots could potentially be more resistant to attack by Pratylenchus, since the abundance of Pratylenchus in roots of plants grown in soils   122 from Sites 1 and 2 was positively associated with percent necrotic root surface area, and negatively associated with total root surface area. Forge et al. (2008) found that paper mulching doubled root biomass, and reduced the number of Pratylenchus penetrans per gram root. This study suggested that reduced nematode damage may be one of the possible reasons for enhanced root growth under mulch.          When soil from Site 3 was used as a growing medium, Pratylenchus abundance in soil and roots was also significantly lower in compost-amended soil than in non-amended soil; however, Pratylenchus abundance in roots did not correlate with percent root necrosis. This suggests that root necrosis of plants grown in Site 3 soil may have been associated with other plant parasitic nematodes, and/ or a fungal complex. No effort was made to identify or monitor fungal pathogens that may have been affecting roots along with Pratylenchus. Site 3 cropped apple before being replanted to cherry in 2013, and previous studies have shown reduced growth when soils were planted with the same, or similar tree species, and the growth reduction was associated with increased frequency in the recovery of plant-pathogenic fungi in roots, including species of Rhizoctonia, Pythium, and Phytophthora (Mazzola 1998, 1999). Sterilizing soil reduces soil microbial activity relative to the non-sterilized (untreated) counterpart. If plant growth is improved in sterilized soil, compared to that in untreated soil, it suggests the microbial community in the untreated soil may be deleterious to plant growth (Mazzola and Mullinix 2005). When plants were grown in soil from Sites 2 and 3, there was no difference in root weight of plants grown in compost- or mulch-amended soil, regardless of soil sterilization. When plants were grown in soil from Site 1, the total root surface area, shoot weight, plant weight, and shoot height increment were greater in compost-amended soil with reduced microbial activity (sterilized), than those in the non-sterilized counterpart; however,   123 there were no differences in any plant growth parameters when plants were grown in sterilized versus non-sterilized mulch-amended soil. In general, these results further suggest that after two growing seasons the amendments may not have greatly influenced soil biology (Yang et al. 2003). Even though amended soil had reduced abundance of Pratylenchus in the bioassay, this finding may be associated with abiotic factors (Stirling et al. 2003), as there was improved nutrient content in compost-amended soil relative to the other treatments. It should be noted that heating of soils to sterilization temperature (120 °C) can cause the mobilization of plant nutrients, such as NH3+, NO3-, and PO43-, which may have accounted for the greater than, or equal growth, of plants grown in sterilized orchard soils, relative to the non-sterilized soils, regardless of soil amendment treatment (Trevors 1996). Unfortunately, soil nutrients were not measured before and after sterilization in this study.  3.4.2 Field and bioassay experiments with soil from Site 4     At this site, the interactive effects of fumigation, pre-plant incorporation of a beef feedlot-based compost, and ‘legacy’ effects of bark mulch and P-fertigation on soil abiotic and biotic properties were assessed through two growing seasons after replanting into this old orchard soil. The ‘legacy’ mulch plots were comprised of shredded bark that had been surface-applied ten years ago, with the residual mulch incorporated into soil at the time of replanting in 2015. In the field, legacy mulch plots contained greater organic carbon and organic matter, and legacy P-fertigated plots contained more P than non-treated controls. However, these soil nutrient differences among treatments in the field did not coincide with increased plant growth in the greenhouse study, or increased microbial abundance and/ or activity in the field experiment.               124  Fumigation strongly reduced Pratylenchus abundance in roots in the field experiment, illustrating the effectiveness of pre-plant fumigation for the control of replant stress at this site. However, regulatory controls now prohibit the use of pre-plant soil fumigation, stressing the need for research to focus on maintaining soil fertility through the application of organic amendments. Therefore, the fact that the compost-amended soil suppressed Pratylenchus relative to the non-amended control in the field was an encouraging result, which has previously been observed in other field studies (Watson et al. 2017). However, the findings from this field study were not in accordance with the greenhouse study, as there was no effect of any of the treatments on Pratylenchus abundance in soil or roots of plants in the greenhouse bioassay. There was a significant block effect for both of these variables in the greenhouse bioassay, which is not surprising, considering the large degree of heterogeneity that existed at this site. Specifically, I observed stunted trees in certain areas of the experimental field site.     The lower abundance of Pratylenchus in compost-amended field soil did not coincide with greater bacterial abundance, and/ or microbial activity in the field plots; however, there were greater CEC, Ca, K, Mg, POXC, organic carbon, and organic matter, as well as a higher fungal abundance and F: B ratio in compost-amended field plots in 2015. Stirling et al. (2003) found a fungal-dominant soil biology to be more suppressive to plant-parasitic nematodes than a soil food web dominated by bacteria. Although no attempt was made in this study to identify the nematode-suppressive mechanisms that were enhanced by the addition of organic matter, the increase in soil health in the field experiment may be responsible for the suppressiveness of the compost treatment through a mechanism of general suppression. General disease suppression is normally enhanced by organic matter input and has been related to increased soil fertility (Bailey and Lazarovits 2003), which is in concordance with the increase in nutrient content caused by the   125 addition of compost.           Findings in the greenhouse study are not in agreement with earlier studies that showed that pasteurization, fumigation, and sterilization positively influence plant growth in disease-conducive, replant stress-prone soils (Mai and Abawi 1981; Jaffee et al. 1982; Mazzola 1998; Van Schoor et al. 2009). Abiotic factors, such as, low or high soil pH, inadequate phosphorus availability, phytotoxins, heavy metal contamination, poor soil structure or drainage, and cold or drought stress, have also been associated with replant stress (Mazzola 1998; Slykuis and Li 1985), and may be at play at Site 4. However, no effort was made in this study to measure most of these variables, other than soil pH and P content. Soil pH was not too low or high, as it was near neutral, regardless of the main factor treatment, and even though the level of soil P was greater in legacy P-fertigated plots in the field, this did not lead to increased plant growth, relative to the untreated control, in the greenhouse study. One treatment in the greenhouse study that did result in greater root length, relative to non-amended soil, was compost-amended field soil. Potted plants grown in mineral soil frequently suffer from waterlogging and poor aeration and, therefore, soils amended with compost may simply have provided a more hospitable environment for plant growth, since increased organic matter confers benefits, such as increased macro-porosity and water-holding capacity (Tindall et al. 1991).   3.4.3 The effect of the amendments on soil biotic and abiotic properties at all four sites  The application method (pre-plant incorporation, or surface application) and physicochmemical composition of the organic amendments may have influenced the results in this study. Compost application affected the chemical composition of soil within the first year after application, regardless of whether the compost was surface applied (Sites 1, 2, and 3), or   126 incorporated into the soil at the time of tree planting (Site 4). However, compost application only influenced soil biology when it was incorporated into soil (Site 4). In addition, the woodchip mulch only affected chemical soil properties when it was incorporated into soil (Site 4), although, the woodchip mulch at this site had also partially decomposed since it was surface applied 10 years prior to being incorporated into soil in 2015. Nevertheless, this study was not designed to compare organic amendment application methods, so further research should consider how differences in organic amendment application method and chemical composition influence soil health properties over time.   3.4.4 Final chapter remarks          Nutrient-rich compost not only affected soil nutrient status at all four sites, it also significantly reduced Pratylenchus abundance in the greenhouse study (Sites 1, 2, and 3) and in the field (Site 4), and potentially mitigated the detrimental effects these organisms can have on young trees. All evidence considered, pre-plant incorporation, or surface application of compost may be a viable practice to increase soil nutrient status of newly-cultivated and replant stress-prone soils. However, more long term studies on the effects of organic amendments in perennial horticulture need to be done. After only a two-year study, it is not clear what long term effects the amendments will have on soil properties and mitigation of soil-borne disease.       127 4.0 Chapter 4: Final Conclusion   This study was particularly interesting because it investigated soils in areas previously considered climatically unsuitable for growing sweet cherries. Soils in the new areas have never been fumigated, and thus they provided a unique opportunity to test methods, such as the application of organic amendments, for retaining and encouraging beneficial soil microbes. As fewer options are now available for soil pathogen control, the application of organic amendments represents a proactive approach to maintain soil health, and mitigate future soil-borne disease in newly established orchard soils (Mazzola 1999).       The first greenhouse bioassay tested soils from 18 newly-cultivated and old orchards distributed throughout the Okanagan Valley. Growth of young fruit trees replanted into old orchard soil is often poor relative to soil that has not previously cropped any tree fruits (Mazzola 1999; Mazzola and Manici 2012). The results from this experiment were largely in agreement with previous research and my initial hypotheses: relative to new orchard soil with reduced microbial activity (sterilized soil), plants grown in untreated (non-sterilized soil) new orchard soil had greater growth. There were likely more beneficial microbial communities, and fewer biological impediments to growth of cherry trees planted in soils that had never cropped sweet cherry or related tree fruits. Furthermore, results from multiple regression analyses indicated that increased soil organic carbon levels and an active microbial community would benefit growth of cherry trees in both new and old orchard soils. Such results emphasize the need for orchard management practices that maintain and increase soil health (i.e. reduced tillage, organic amendment application, decreased pesticide application) in the new orchard soils, so as to foster the longevity of present and future tree plantings. Future studies should measure other important soil health indicators in new and old orchard soils, such as microbial diversity, and other plant pathogens affecting plant growth, along with Pratylenchus, and consider how such factors may   128 also contribute to predicting cherry tree growth.        Other studies have shown that soil biological transformations that allow for the development of root lesion nematode populations, as well as root diseases caused by fungal pathogens, can take place within three to four years of orchard establishment (Mazzola 1999). Because high microbial activity often suppresses plant parasitic nematode populations, this observation further indicates the importance of retaining and encouraging beneficial soil microbes, and maintaining soil health at the onset of orchard establishment. In the field experiments, the surface applied woodchip mulch did not influence soil nutrient status, compared to the non-amended soil. However, woodchip mulch did influence soil organic matter and total carbon at Site 4, where it was incorporated into soil, after having been a surface mulch for ten years. The nutrient-rich compost increased soil nutrient status in the field experiments at all four sites, and I hypothesized that this result would coincide with greater microbial activity and abundance, but this was not the case. There were few effects of compost application, and none for woodchip mulch, on the measured soil biological parameters in the field experiments. In both the field and bioassay experiments, I hypothesized that compost-amended soil would have lower Pratylenchus abundance in roots and soil, than those in the non-composted control. Compost-amended soil reduced Pratylenchus root colonization in the field at one site (Site 4), compared to that in the non-composted control. In the second bioassay, plants grown in soils amended with compost or woodchip mulch in the field (Sites 1, 2, and 3) had reduced Pratylenchus root colonization in the greenhouse, relative to that in the non-amended soil. The fact that there was a lower abundance of Pratylenchus in compost-amended soil from Sites 1, 2, and 3 in the bioassay, but not the field experiment, and visa-versa for Site 4, was not a conclusive result.     129  In the second bioassay, I hypothesized that sterilizing compost- and mulch-amended soil would be detrimental to plant growth compared to the non-sterile counterparts. I rationalized that amended soil should have a more active and diverse microbial community than that in non-amended soil, thereby supporting the plant in mineral nutrient uptake by means of organic matter decomposition. However, this was not the case, as there was no difference between the sterilized compost- and mulch-amended soils relative to their respective non-sterile counterparts. It seems possible that after only two years the amendments had not greatly influenced soil biology.   In order to assess the effect of organic management practices on soil properties it is common to sample soil from a 0-30 cm depth (Meyer et al. 2015; Olson and Al-Kaisi 2015). However, after two years, the influence of surface amendments' on soil biology may only have been exerted in the top few centimeters of soil (Yang et al. 2003). Therefore, the method of taking 0-30 cm soil cores from treatment plots may have diluted and disguised any soil amendment effects that were present (Yang et al. 2003). Future research with surface applied organic amendments should sample soil from the 0-15 cm and 15-30 cm fractions of the soil profile, in recognition that biological properties vary at a fine scale (Yang et al. 2003).   The quantity and quality of soil organic amendments, as well as numerous other factors, dictate the influence they can have on soil health properties, and in turn, the development of a disease suppressive soil (Aryantha 2000; Litterick et al. 2004; Fontaine et al. 2007). In a meta-analysis by Bonanomi et al. (2007), it was found that disease suppressiveness increased with application rate in about half of the experiments assessed, and this result was also noted by Noble and Coventry (2005). Therefore, high and frequent application rates were necessary in many published studies to achieve disease control. Thus, despite experimental evidence of effectiveness in many studies, the cost of purchasing a suitable amendment and transporting it to   130 the required site may be prohibitive, even with high value crops, such as cherry. This may be a limitation of the current study, since compost and mulch were applied at a generous rate of  0.05 m3 m-2 in the spring of the first and second growing seasons. This may not be an economically realistic application rate and frequency for many commercial growers.   Another limitation of the field study was that it was only two years long. Most soil organic management studies have only been short-term, spanning just a few years, while few have evaluated the long-term (i.e. a decade or more) effects of soil management practices on tree yield, growth, and soil properties (Glenn and Welker, 1996; Klik et al., 1998; Morlat and Chaussod, 2008; Tasseva, 2008; Atucha et al. 2011). Longer studies that span the lifetime of commercial orchards are helpful in order to better understand changes over time, as well as year-to-year variability in perennial crop systems. For example, Atucha et al. (2011) found that after 16 years of mulch application, tree growth was greater and soil under the mulch had higher total carbon and total nitrogen, relative to that in the control treatment.   Greenhouse bioassay studies also have their limitations. Since the soil bioassays were conducted in controlled growth chambers and greenhouses, any conclusions drawn from the results in these studies do not necessarily translate to the field. Future research should consider the use of microplots to study plant growth in the field. To establish microplots, holes are dug to a 1-2 m depth and 0.5 m diameter, and field soil is set aside (Barker et al. 1979). Then cement barriers or corrugated PVC (polyvinyl chloride) tubing is established in the hole. One could then sterilize the field soil before putting it back into the plot, or fumigate the soil, in order to study the plant growth response to soil sterilization, under a more controlled field setting.   The practice of soil sterilization to study the influence of soil biology on plant growth also has drawbacks. Heating of soils to sterilization temperature (120 °C) can cause the   131 mobilization of plant nutrients, such as NH3+, NO3-, and PO43- (Trevors et al. 1996). Although soil nutrient status was not measured before and after soil sterilization in this study, future research should do so in order to more accurately draw conclusions from the results. For instance, if plant growth is greater in sterilized soil than that in the untreated counterpart, it may result in incorrect conclusions about the ‘health’ of the soil microbial community to be made. An alternative way to confirm that a soil is biologically suppressive, or conducive, to soil pathogens, is to sterilize the respective soil, and then add 0.1% to 10% of the unsterilized soil to the sterilized soil (Weller et al. 2002). Future studies should employ such techniques to measure soil suppressiveness, as the impact of changes to soil abiotic factors due to sterilization are minimized when suppressive or conducive soils are diluted into a common background of sterilized soil, allowing for a direct comparison of the introduced microbiological components (Weller et al. 2002).           All evidence considered, pre-plant incorporation, or surface application of compost may be a viable option to increase soil nutrient status after two growing seasons in both newly-cultivated and older orchard soils. Compost application may also maintain soil health, and mitigate future soil-borne disease in newly established orchard soils that have never cropped sweet cherry or other tree fruits. Woodchip mulch may be an effective tool to increase soil organic matter content if it is incorporated into soil (Site 4); however, when surface applied, for only two growing seasons, no effects on soil nutrient status and soil biology were detectable (Sites 1, 2, and 3). Overall, the use of organic amendments may be an effective tool to maintain and/ or restore soil organic carbon in perennial horticulture. 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Sci. 44: 800–809  Zunke, U. 1990. Ectoparasitic feeding behavior of the root lesion nematode, Pratylenchus penetratns, on root hairs of different host plants. Revue Nematologie: 13: 331-337.    151 Appendices Appendix A: Additional information for Chapter 2   Figure A.2.1 Standard curve for the fluorescein diacetate (FDA) hydrolysis assay. The concentration of fluorescein produced (µg g-1) as a function of optical density at 490 nm (OD490) generates a standard curve against which the OD490 of the actual samples can be compared. The OD490 of the sample can be used to determine the amount of fluorescein produced (µg g-1) by solving for x in the equation y = 0.0627x + 0.0829 (r2=0.99).   00.20.40.60.811.21.41.61.820 5 10 15 20 25 30 35OD490fluorescein produced µg g-1)  152 Table A.2.1 Shoot height increment of plants grown in non-sterile and sterile soil for each site. Values represent mean (n=5) and standard deviation (SD).       ANOVA Resultsa   Shoot Height Increment (cm)  Treatment (df=1) Block (df=5) Treatment*Block (df=5) Orchard Type Site Non-sterile SD Sterile SD   F-value P-valueb F-value P-value F-value P-value Non-cultivated 1 157.6 2.6 95.5 0.88   2088 <0.001 0.5 0.6 19 0.04 Non-cultivated 2 124.7 2.8 113.5 9.4  5.2 0.06 3.8 0.2 0.1 0.8 New 3 116.9 10.2 122.9 5.9  1.0 0.3 0.06 0.9 0.3 0.6 New 4 131.2 2.8 114.4 3.1  70 <0.001 0.18 0.8 0.3 0.7 New 5 155.5 3.9 106.4 2.8  414 <0.001 0.7 0.5 0.1 0.7 New 6 149.3 4.3 125.3 5.5  46 <0.001 0.08 0.9 6.1 0.1 Old 7 106.7 1.8 127.9 8.6  22 0.003 2.1 0.9 0.06 0.8 Old 8 136.8 14.9 115.9 3.3  7.5 0.03 0.4 0.7 4.3 0.1 Old 9 99.7 2.5 153.1 4.3  472 <0.001 4.3 0.1 0.1 0.8 Old 10 105.4 4.1 118.5 8.3  8.0 0.03 0.3 0.7 12 0.07 Old 11 125.5 2.8 99.23 2.3  208 <0.001 0.1 0.8 2.5 0.2 Old 12 88.6 6.7 117.8 3.5  60 <0.001 1.7 0.2 0.1 0.7 Old 13 94.9 1.5 124.4 5.0  125 <0.001 1.9 0.3 0.3 0.7 Old 14 71.1 0.7 120.9 4.7  436 <0.001 0.7 0.5 17 0.06 Old 15 96.2 5.0 128.3 2.4  161 <0.001 0.2 0.7 1.1 0.4 Old 16 136.7 17.1 119.8 12.4  2.6 0.2 0.1 0.8 20 0.4 Old 17 84.4 4.3 99.0 3.9  25 0.002 0.05 0.9 4.3 0.2 Old 18 123.2 20.9 142.9 10.7   2.8 0.1 1.2 0.4 0.2 0.8 a = ANOVA results compare the mean root weight of plants grown in sterile or non-sterile soil from the same site. b = Results are significant at a P≤0.003 significance level due to the Bonferroni correction procedure.                            153 Table A.2.2 The eigenvalues were used to decide the number of axes to represent and display in the plot on the basis of the amount of total variance explained. The largest amount of the variance was explained by the first two principal components.  Total Variance Explained         Component Initial Eigenvalues   Extraction Sums of Squared Loadings  Total % of Variance Cumulative % Total % of Variance Cumulative % 1 7.206 39.6 21.3 7.206 39.6 39.6 2 4.184 21.3 60.9 4.184 21.3 60.9 3 2.168 11.412 72.312    4 1.382 6.321 78.633    5 1.12 5.893 84.526    6 0.899 4.732 89.258    7 0.568 2.989 92.248    8 0.422 2.223 94.471    9 0.374 1.967 96.438    10 0.255 1.342 97.78    11 0.122 0.645 98.425    12 0.113 0.596 99.021    13 0.081 0.428 99.449    14 0.052 0.274 99.723    15 0.026 0.139 99.862    16 0.019 0.1 99.962    17 0.007 0.038 100    18 4.70E-17 2.47E-16 100    19 -2.60E-16 -1.37E-15 100                          154 Statistical Models in R R code for Principal Components Analysis (PCA)  # Load requires packages library (ade4) library (vegan) library (glus) library (ape0  # Import the data from CSV files bioassay1 <- read.csv (“bioassay1_PCA”) # log transform log.bioassay1 <- log(bioassay[, 1:18]) # apple PCA on full dataset – scale = TRUE  bioassay1.pca <- prcomp(log.bioassay1, center = TRUE, scale. = TRUE) # print method print(bioassay1.pca) # summary method summary(bioassay1.pca) # eigenvalues ev <- bioassay 1.pca$CA$eig) # Apply Kaiser-Guttman criterion to select axes ev {ev > mean(ev)] # Plot eigenvalues and % variance for each axis par (mfrow=c (2, 1)) barplot (ev, main=”Eigenvalues”, col=’bisque”, las=2) abline (h=mean(ev), col=”red”)  #Predict PCs  predict(log.bioassay1, newdata=tail(log.bioassay1, 2)) # Plot using biplot.rda par(mfrow=c(1,2)) biplot.rda(env.pca, main=”PCA – scaling 2”             155 Appendix B: Additional information for Chapter 3  Table B.3.1 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with the factors site (Sites 1, 2, and, 3) and soil amendment (bare, compost, and/ or mulch) and site (Sites 1, 2, and, 3) on cation exchange capacity (CEC), electrical conductivity (EC), and elements phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na). Values represent means (overall and for each sampling year) and standard deviation (SD). Degrees of freedom (df), and P-and F-values for each variable from the ANOVA are given. Year Amendmenta CEC (meq 100 g-1 soil) SD P  (mg  kg-1) SD Ca (mg  kg-1) SD K (mg  kg-1) SD Mg  (mg  kg-1) SD Na  (mg  kg-1) SD EC (S m-1) SD Overall (n=72) bare 19.9 b 2.0 150 b 34 2993 b 419 434 b 186 293 b 80 39.5 b 8.0 0.4 ab 0.1  compost 22.4 a 1.9 203 a 43 3123 a 280 1030 a 283 383 a 105 69.2 a 12.0 0.6 a 0.2  mulch 17.6 b 2.7 148 b 30 2936 b 306 432 b 148 286 b 82 35.2 b 5.6 0.2 b 0.1                 2015 (n=36) bare 16.1 1.9 158 b 47 2108 452 436  214 234 56 33.3  9.1 0.4  0.1  compost 19.4 1.6 256 a 60 2302 254 1087  316 371 89 56.2  12.7 0.7  0.3  mulch 16.3 2.6 167 b 38 2046 260 409  132 225 45 29.8  4.0 0.3  0.1                 2016 (n=36) bare 23.8 2.1 143 21 3878 387 433  159 353 104 45.8  6.9 0.3  0.1  compost 25.5 2.2 151 25 3944 307 974  250 395 122 82.3  11.2 0.4  0.2  mulch 18.9 2.9 129 22 3826 352 455  164 348 119 40.6  7.2 0.2  0.1 ANOVA  Resultsb df P F P F P F P F P F P F P F Site 1 <0.001 318 <0.001 88 <0.001 384 <0.001 20 0.04 3.4 <0.001 194 <0.001 109  Amendment 2 <0.001 39 <0.001 27 0.018 4.3 <0.001 172 <0.001 14 <0.001 186 <0.001 40  Site * Amendment 4 0.03 2.8 0.2 1.6 0.27 1.3 <0.001 6.0 0.4 1.0 <0.001 6.1 0.2 1.4  Year 1 <0.001 779 <0.001 503 <0.001 1839 <0.001 22 0.4 0.6 <0.001 280 <0.001 28  Year * Site 2 <0.001 238 <0.001 185 <0.001 293 0.003 6.2 <0.001 22.9 <0.001 67 0.003 6.4  Year * Amendment 2 0.5 0.7 <0.001 41 0.3 1.2 0.05 3.4 0.1 2.4 <0.001 24 0.007 5.5  Year * Site *  Amendment 4 <0.001 3.9 0.1 1.9 0.8 0.4 <0.001 12.7 0.6 0.7 0.003 4.7 0.016 3.4 a = Amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.    156 Table B.3.2 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and site (Sites 1, 2, and, 3) on permanganate oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio, and pH. Values represent means (overall and for each sampling year) and standard deviation (SD). Degrees of freedom (df), and P-and F-values for each variable from the ANOVA are given. Year Amendmenta mg POXC kg-1  soil SD TN (%) SD TC (%) SD OC (%) SD OM (%) SD C: N SD pH SD Overall (n=72) bare 1313 b 238 0.3 b 0.06 3.9 b 0.5 3.9 b 0.5 7.0 b 0.9 11.5 1.1 6.3 b 0.5  compost 1503 a 272 0.4 a 0.09 4.8 a 0.7 4.8 a 0.7 8.4 a 1.2 11.6 0.8 6.7 a 0.2  mulch 1324 b 224 0.3 b 0.06 4.1 b 0.5 4.0 b 0.4 7.2 b 0.8 11.6 0.8 6.4 b 0.2                 2015 (n=36) bare 1661 b 364 0.36 b 0.07 4.1 b 0.7 4.0 b 0.5 6.6 b 0.9 10.6 1.7 6.3  0.3  compost 1995 a 440 0.51 a 0.13 5.4 a 1.0 5.5 a 0.9 8.9 a 1.6 11.4 1.2 6.5 0.3  mulch 1682 b 346 0.37 b 0.08 4.3 b 0.6 4.3 b 0.4 6.9 b 0.7 11.0 1.1 6.2 0.3                 2016 (n=36) bare 964 111 0.32  0.04 3.7 0.4 3.7 0.4 7.4 0.8 12.4 0.6 6.2 0.8  compost 1010 104 0.35  0.05 4.1 0.4 4.0 0.4 7.8 0.7 11.8 0.5 6.8  0.2  mulch 966 103 0.31  0.04 3.8 0.4 3.7 0.4 7.5 0.8 12.2 0.5 6.5  0.2 ANOVA Resultsb df P F P F P F P F P F P F P F  Year 1 <0.001 603 <0.001 116 <0.001 133 <0.001 161 0.2 1.8 <0.001 41 0.024 5.4  Year * Site 2 <0.001 10 0.1 2.3 0.05 3.0 0.019 4.3 0.4 0.9 <0.001 34 0.5 0.8  Year * Amendment 2 0.001 8.1 <0.001 20 <0.001 21 <0.001 29 <0.001 34 0.5 0.6 0.2 1.8  Year * Site *  Amendment 4 0.6 0.7 0.2 1.6 0.2 1.5 0.3 1.1 0.2 1.5 0.4 1.1 0.6 0.8  Site 1 <0.001 17 <0.001 177 <0.001 339 <0.001 358 <0.001 353 <0.001 30 <0.001 59  Amendment 2 <0.001 10 <0.001 26 <0.001 29 <0.001 36 <0.001 30 0.08 2.7 <0.001 10  Site * Amendment 4 0.9 0.3 0.1 2.1 0.1 2.0 0.19 1.6 0.3 1.3 0.04 2.6 0.2 1.5 a = Amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test b = Values are significant at a P ≤ 0.05 significance level.        157 Table B.3.3 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and site (Sites 1, 2, and, 3) on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S copy number) and fungi (18S copy number), fungal-to-bacterial (F: B) ratio, and % arbuscular mycorrhizal fungi (AMF) colonization. Values represent means (overall and for each sampling year) and standard deviation (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta FDA hydrolysis  (µg g-1) SD log(16S copy #  g-1 dry soil) SD log(18S copy # g-1 dry soil) SD F: B Ratio SD % AMF Colonization SD Overall (n=72) bare 3.7 1.2 9.15 0.365 7.7 0.405 0.84 0.05 27.9 9.83  compost 4.1 1.8 9.25 0.435 7.75 0.375 0.835 0.05 21.3 8.27  mulch 4.4 2.0 9.25 0.42 7.8 0.365 0.845 0.055 22.6 8.32             2015 (n=36) bare 2.1 0.3 9.4 0.32 8.2 0.38 0.87 0.05 41.8 8.29  compost 2.3 0.2 9.5 0.58 8.3 0.30 0.88 0.07 37.4 9.81  mulch 2.1 0.30 9.4 0.54 8.2 0.31 0.88 0.08 38.8 10.60             2016 (n=36) bare 5.3 2.28 8.9 0.41 7.2 0.43 0.81 0.05 14.0  11.37  compost 5.9 3.49 9.0 0.29 7.2 0.45 0.79 0.03 5.3  6.74  mulch 6.8 3.74 9.1 0.30 7.4 0.42 0.81 0.03 6.5  6.05 ANOVA Resultsb df P F P F P F P F P F  Site 1 <0.001 35 <0.001 18 <0.001 120 0.008 5.3 0.001 7.7  Amendment 2 0.5 0.8 0.5 0.6 0.9 0.1 0.7 0.3 <0.001 10  Site * Amendment 4 0.6 0.7 0.8 0.3 0.4 1.0 0.7 0.4 0.04 2.7  Year 1 <0.001 154 <0.001 60 <0.001 831 <0.001 138 <0.001 864  Year * Site 2 <0.001 18 <0.001 16 <0.001 151 <0.001 20 <0.001 12  Year* Amendment 2 0.5 0.7 0.2 1.8 0.09 2.5 0.2 1.6 0.001 8.7  Year * Site *   Amendment 4 0.5 0.8 0.9 0.3 0.2 1.6 0.7 0.4 0.014 3.5 a = Amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test b = Values are significant at a P ≤ 0.05 significance level.         158 Table B.3.4 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and site (Sites 1, 2, and, 3) on Pratylenchus spp. abundance in 1 g root and 50 g bulk soil, and total nematodes in 50 g bulk soil. Values represent means (overall and for each sampling year) and standard deviation (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta Pratylenchus spp. g-1 root SD Pratylenchus spp. 50 g-1 soil SD Total Nematode 50 g-1 soil SD Overall (n=72) bare 133 240 23 16 232 143  compost 103 132 18 13 289 162  mulch 161 352 21 14 251 127         2015 (n=36) bare 136 221 18 14 260 189  compost 169 206 12 8 354 203  mulch 200 387 15 10 289 133         2016 (n=36) bare 131 259 27 18 204 96  compost 37 58 25 18 224 121  mulch 123 318 28 18 214 121 ANOVA Resultsb df P F P F P F  Site 1 0.003 6.3 0.001 8.1 <0.001 15.3  Amendment 2 0.05 2.3 0.06 2.3 0.06 3.1  Site * Amendment 4 0.2 1.6 0.3 1.3 0.2 1.3  Year 1 <0.001 18 <0.001 53 <0.001 26.6  Year * Site 2 <0.001 26 <0.001 12 <0.001 25.5  Year * Amendment 2 0.3 1.3 0.06 3.1 0.1 1.7  Year * Site *   Amendment 4 0.4 1.1 0.06 0.9 0.8 0.8 a = Amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.         159 Table B.3.5 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on cation exchange capacity (CEC), electrical conductivity (EC), and elements phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) at Site 1. Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta   CEC (meq 100 g-1 soil) SD P  (mg  kg-1) SD Ca (mg  kg-1) SD K (mg  kg-1) SD Mg  (mg  kg-1) SD Na  (mg  kg-1) SD EC (S m-1) SD Overall Bare  21.5 b 3.3 147 55 3107 411 551 b 231 297 b 65 50.9 b 14.2 0.7 ab 0.2 (n=24) Compost  25.2 a 2.0 202 55 3324 304 1290 a 370 415 a 90 90.4 a 16.4 0.9 a 0.5  Mulch  21.8 b 1.5 139 30 3018 277 453 b 133 265 b 35 47.5 b 9.5 0.2 b 0.3                  2015 Bare  16.6  3.8 155 b 68 1901 275 574 378 223 83 41.8  18.8 0.7 b 0.2 (n=12) Compost  20.4  2.0 275 a 82 2150 284 1245 551 387 121 70.8  20.8 1.2 a 0.5  Mulch  18.5  1.1 170 b 37 1851 174 447 203 212 39 39.2  6.5 0.6 b 0.3                  2016 Bare  26.4  2.8 139 41 4312 548 528 84 370 46 60.1  9.4 0.7 a 0.3 (n=12) Compost  29.9  2.1 128 27 4498 323 1335 189 443 58 110  11 0.6 ab 0.3  Mulch  25.0  2.1 108 22 4184 381 459 62 318 31 55.7  12.4 0.4 b 0.3  ANOVA Resultsb df P F P F P F P F P F P F P F  Amendment 2 <0.001 13 0.001 9.8 0.1 2.6 <0.001 78.5 <0.001 35.8 <0.001 54 0.001 9.5   Block 5 0.4 1.04 0.7 0.6 0.8 0.5 0.5 0.9 0.5 0.9 0.9 0.2 0.1 1.8    Block * Amendment 10 0.4 1.1 0.3 1.4 0.8 0.6 0.3 1.3 0.3 1.3 0.6 0.8 0.1 1.9   Year 1 <0.001 232 <0.001 51 <0.001 1624 0.8 0.1 <0.001 44.7 <0.001 97 0.001 14   Year * Amendment 2 0.05 3.6 <0.001 13 0.8 0.2 0.7 0.3 0.08 2.9 0.08 2.9 0.02 4.4   Year * Block 5 0.4 1.1 0.3 1.4 0.3 1.4 0.7 0.6 0.5 0.9 0.09 2.3 0.5 0.7   Amendment * Year * Block 10 0.9 0.3 0.2 1.5 0.4 1.1 0.1 1.8 0.1 1.9 0.03 2.3 0.7 0.6 a = Soil amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.   160 Table B.3.6 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on on permanganate oxidizable carbon (POXC), total nitrogen (TN), total carbon (TC), inorganic carbon (OC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio and pH at Site 1. Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta   mg POXC kg-1 soil SD TN  (%) SD TC  (%) SD OC  (%) SD OM (%) SD C: N SD pH SD Overall Bare  1385 187 0.47 0.078 5.1 0.7 5.1 0.1 5.1 0.7 9.2 b 1.2 5.7 b 0.4 (n=24) Compost  1566 220 0.57 0.11 6.3 0.9 6.1 0.1 6.3 0.8 11.1 a 1.5 6.1 a 0.3  Mulch  1444 202 0.47 0.065 5.4 0.4 5.1 0.1 5.3 0.3 9.5 b 0.6 5.7 b 0.3                  2015 Bare  1709 b 277 0.49 b 0.090 5.4 b 1.1 5.4 b 0.9 8.8 b 1.5 10.9 0.8 5.8 0.4 (n=12) Compost  2022 a 383 0.65 a 0.16 7.2 a 1.5 7.3 a 1.3 11.8 a 2.2 11.3 1.8 5.9 0.4  Mulch  1795 ab 138 0.49 b 0.079 5.7 b 0.5 5.7 b 0.3 9.2 b 0.5 11.8 1.1 5.6 0.3                  2016 Bare  1062  97 0.45  0.066 4.9 0.5 4.9 0.5 9.7 0.9 11 0.6 5.6  0.3 (n=12) Compost  1110  57 0.48  0.052 5.4 0.4 5.3 0.4 10.3 0.8 11.2 0.4 6.2  0.2  Mulch  1094  64 0.44  0.050 5.1 0.4 5.0 0.4 9.9 0.7 11.5 0.6 5.8  0.2  ANOVA Resultsb df P F P F P F P F P F P F P F   Amendment 2 0.007 6.7 0.002 9.4 <0.001 13 <0.001 15.8 0.001 11 0.02 4.7 0.01 5.9   Block 5 0.005 4.9 0.2 1.5 0.07 2.4 0.2 1.7 0.1 1.7 0.1 1.8 0.4 1.1    Block * Amendment 10 0.3 1.2 0.3 1.2 0.2 1.5 0.3 1.2 0.5 0.9 0.2 1.5 0.9 0.3   Year 1 <0.001 409 <0.001 26 <0.001 42 <0.001 55.3 0.9 0.01 0.5 0.4 0.2 1.6   Year * Amendment 2 0.01 5.1 0.007 6.9 0.002 8.9 0.001 10 <0.001 14.6 0.8 0.2 0.1 2.3   Year * Block 5 0.008 4.4 0.009 4.3 0.1 1.9 0.2 1.7 0.2 1.7 <0.001 9.8 0.4 1.1   Amendment * Year * Block 10 0.2 1.3 0.09 2 0.05 2.3 0.1 1.8 0.05 2.4 0.1 1.9 0.3 1.2 a = Soil amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.    161 Table B.3.7 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S), and total fungi (18S), fungal-to-bacterial (F: B) ratio, and the % root colonization by arbuscular mycorrhizal fungi (AMF) at Site 1. Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta  FDA hydrolysis (µg g-1) SD log(16S copy #  g-1 dry soil) SD log(18S copy # g-1 dry soil) SD F: B  Ratio SD % AMF Colonization SD Overall Bare  4.9 1.2 9.4 0.4 8.2 0.3 0.91 0.060 21.2 8.2 (n=24) Compost  5.4 1.3 9.5 0.3 8.4 0.3 0.92 0.051 22.2 7.3  Mulch  5.1 0.7 9.5 0.5 8.3 0.3 0.91 0.060 20.1 4.2              2015 Bare  2.7 0.5 9.3 0.7 8.2 0.3 0.87 0.038 34 b 5.8 (n=12) Compost  2.8 0.2 9.4 0.1 8.4 0.4 0.88 0.070 40 a 7.8  Mulch  2.7 0.1 9.5 0.2 8.3 0.3 0.89 0.11 39 a 6.4              2016 Bare  7.1 1.9 9.4 0.2 8.2 a 0.3 0.88 0.074 8.3 10.5 (n=12) Compost  7.9 2.5 9.6 0.5 7.9 b 0.3 0.83 0.027 4.4 6.7  Mulch  7.5 1.4 9.4 0.7 8.2 a 0.3 0.86 0.019 1.2 1.9  ANOVA Resultsb df P F P F P F P F P F   Amendment 2 0.8 0.2 0.9 0.09 0.3 1.2 0.4 0.8 0.3 1.3   Block 5 0.2 1.7 0.2 1.4 0.1 2.1 0.2 1.5 0.3 1.1   Block * Amendment 10 0.9 0.3 0.2 1.5 0.4 1.1 0.1 1.8 1.2 0.376  Year 1 <0.001 412 0.8 0.06 0.002 12 0.2 2.1 <0.001 313.1   Year *  Amendment 2 0.3 1.1 0.3 1.2 0.004 7.5 0.2 1.6 0.026 4.5   Year * Block 5 0.08 2.3 0.6 0.7 <0.001 20 0.002 6.1 0.5 0.8   Amendment * Year * Block 10 0.2 1.5 0.5 0.8 <0.001 7.3 0.6 0.7 0.4 1.1 a = Soil amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.   162 Table B.3.8 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on Pratylenchus spp. abundance in 1 g root and 50 g bulk soil, and total nematodes in 50 g bulk soil at Site 1 in. Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta   Pratylenchus spp. g-1 root SD Pratylenchus spp. 50 g-1 soil SD Total Nematodes 50 g-1 soil SD Overall Bare  183 350 21 12 274 b 89 (n=24) Compost  175 201 21 14 369 a 186  Mulch  233 528 21 8 273 b 109          2015 Bare  148 209 20 8 389 115 (n=12) Compost  313 326 15 7 521 247  Mulch  452 1017 20 7 390 156          2016 Bare  217 492 21 17 158 64 (n=12) Compost  37 76 26 21 216 125  Mulch  13 38 21 8 156 62  ANOVA Resultsb df P F P F P F   Amendment 2 0.5 0.6 0.8 0.1 0.01 5.9   Block 5 0.5 0.8 0.2 1.4 0.05 2.6    Block * Amendment 10 0.3 1.2 0.3 1.2 0.2 1.4  Year 1 <0.001 18 0.5 0.3 <0.001 54   Year * Amendment 2 0.3 1.3 0.5 0.8 0.6 0.4   Year * Block 5 0.4 1.2 0.4 1.1 0.08 2.3   Amendment *Year * Block 10 0.4 1.1 0.4 1.1 0.6 0.7 a = Soil amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.     163 Table B.3.9 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on cation exchange capacity (CEC), electrical conductivity (EC), and nutrients phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) at Site 2.  Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta   CEC (meq 100 g-1 soil) SD P  (mg  kg-1) SD Ca (mg  kg-1) SD K (mg  kg-1) SD Mg  (mg  kg-1) SD Na  (mg  kg-1) SD EC (S m-1) SD Overall Bare  23.4 b 1.5 197 33 3877 504 374 b 129 273 28 52 b 7 0.3 b 0.1 (n=24) Compost  25.3 a 1.1 229 39 3831 188 977 a 188 336 29 70 a 9 0.5 a 0.1  Mulch  23.5 b 1.3 199 33 3831 240 440 b 123 270 29 42 b 5 0.2 b 0.9                  2015 Bare  18.1 1.6 183 b 34 2610 761 310  127 205 b 24 42 ab 6 0.4 ab 0.1 (n=12) Compost  19.3 1.3 249 a 42 2673 214 785  158 293 a 40 51 a 7 0.6 a 0.2  Mulch  17.3 1.5 195 b 32 2539 265 386  115 205 b 27 34 c 3 0.2 c 0.05                  2016 Bare  29.8 1.4 210 33 5143 248 438  130 341 32 61 b 8 0.2 b 0.1 (n=12) Compost  31.3 1.0 209 37 4988 162 1169  217 379 18 88 a 11 0.4 a 0.1  Mulch  29.8 1.2 203 33 5122 216 494  131 335 31 50 b 7 0.2 b 0.1  ANOVA Resultsb df P F P F P F P F P F P F P F  Amendment 2 0.002 9.3 0.002 9.1 0.8 0.2 <0.001 74 <0.001 21 <0.001 65 <0.001 46   Block 5 0.1 1.9 0.5 0.9 0.2 1.6 0.07 2.4 0.38 1.1 0.04 2.8 0.1 1.8    Block * Amendment 10 0.2 1.5 0.9 0.3 0.2 1.5 0.9 0.3 0.2 1.5 0.9 0.3 0.01 3.3   Year 1 <0.001 1701 <0.001 840 <0.001 703.8 <0.001 146.1 <0.001 473 <0.001 422 <0.001 17   Year * Amendment 2 0.5 0.6 0.002 8.9 0.4 0.7 0.4 0.7 0.003 8.3 <0.001 30 0.04 3.8   Year * Block 5 0.3 1.3 0.4 0.9 0.9 0.4 0.09 2.2 0.3 1.1 0.01 4.2 0.4 1.1   Amendment * Year * Block 10 0.9 0.3 0.2 1.5 0.4 1.1 0.1 1.8 0.9 0.3 0.2 1.5 0.2 1.5 a = Soil amendment sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.    164 Table B.3.10 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on permanganate oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio, and pH at Site 2. Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta   mg POXC kg-1 soil SD TN  (%) SD TC  (%) SD OC  (%) SD OM  (%) SD C: N SD pH SD Overall Bare  1238 143 0.31 0.033 4.3 0.4 4.2 0.3 7.4 0.5 12.3 b 0.5 6.4 b 0.1 (n=24) Compost  1452 131 0.42 0.038 4.7 0.3 4.7 0.3 8.4 0.6 12.1 b 0.5 6.8 a 0.2  Mulch  1237 132 0.31 0.032 4.35 0.3 4.2 0.2 7.6 0.4 12.6 a 0.5 6.8 a 0.2                  2015 Bare  1487 b 193 0.37 b 0.045 4.6 b 0.5 4.4 b 0.3 7.0 b 0.5 12.2 0.5 6.5 0.3 (n=12) Compost  1920 a 185 0.44 a 0.051 5.2 a 0.5 5.3 a 0.5 8.6 a 0.8 11.9 0.8 6.6 0.2  Mulch  1497 b 199 0.37 b 0.045 4.6 b 0.4 4.5 b 0.2 7.4 b 0.3 12.4 0.7 6.5 0.3                  2016 Bare  990 92 0.32 0.022 4.0 0.2 4.0 0.2 7.8 0.5 12.4 0.4 6.4 0.2 (n=12) Compost  984 77 0.34 0.025 4.2 0.1 4.1 0.1 8.1 0.3 12.2 0.5 7.1 0.2  Mulch  978 64 0.32 0.020 4.1 0.2 3.9 0.2 7.9 0.5 12.7 0.4 7.0 0.1  ANOVA Resultsb df P F P F P F P F P F P F P F  Amendment 2 0.001 10 0.011 5.9 0.03 4.1 <0.001 12 0.001 11 0.02 4.5 <0.001 46   Block 5 0.9 0.3 0.6 0.7 0.09 2.2 0.08 2.3 0.03 3.1 0.07 2.4 0.1 1.8    Block * Amendment 10 0.9 0.5 0.2 1.5 0.4 1.1 0.1 1.8 0.9 0.51 0.4 1.1 0.01 3.3   Year 1 <0.001 444 <0.001 11828 <0.001 109 <0.001 228 0.05 11 0.05 4.2 <0.001 2.2   Year * Amendment 2 <0.001 21 0.009 6.3 0.007 6.6 <0.001 28 <0.001 19 0.07 3.1 0.4 0.8   Year * Block 5 0.6 0.6 0.3 0.01 0.08 2.3 0.1 1.6 0.3 1.2 0.8 0.1 0.4 0.8   Amendment * Year* Block 10 0.4 1.1 0.1 0.8 1.1 0.1 1.8 0.9 0.2 1.5 0.4 1.1 0.1 0.8 a = Soil amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.    165 Table B.3.11 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S), and total fungi (18S), fungal-to-bacterial (F: B) ratio, and the % root colonization by arbuscular mycorrhizal fungi (AMF) at Site 2. Values represent means (overall and for each year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta   FDA hydrolysis (µg g-1) SD log(16S copy # g-1 dry soil) SD log(18S copy # g-1 dry soil) SD F: B  Ratio SD % AMF Colonization SD Overall Bare   2.3 0.8 8.9 0.3 7.3 0.2 0.81 0.040 38.0 13.6 (n=24) Compost  2.3 1.0 9.1 0.4 7.5 0.3 0.81 0.041 31.0 9.4  Mulch  2.2 0.8 9.0 0.5 7.4 0.3 0.81 0.054 35.0 9.4              2015 Bare  1.6 0.6 9.2 0.4 8.0 0.1 0.87 0.057 38.0 11.8 (n=12) Compost  1.7 0.6 9.3 0.4 8.1 0.1 0.87 0.041 31.0 16.1  Mulch  1.7 0.6 9.2 0.6 7.9 0.2 0.87 0.083 35.0 13.3              2016 Bare  2.9 0.9 8.6 0.1 6.6 0.3 0.77 0.023 23.0 a 15.5 (n=12) Compost  2.9 1.4 8.9 0.4 6.8 0.4 0.77 0.029 2.8 b 2.7  Mulch  2.6 1.0 8.7 0.3 6.8 0.4 0.77 0.023 5.8 b 5.4  ANOVA Resultsb df P F P F P F P F P F  Amendment 2 0.8 0.1 0.4 0.8 0.5 0.6 0.9 0.03 0.002 9.5   Block 5 0.9 0.2 0.03 3.1 0.6 0.7 0.01 3.7 0.7 0.6    Block * Amendment 10 0.4 1.1 0.9 0.3 0.9 0.3 0.9 0.4 0.8 0.5   Year 1 0.001 17 <0.001 25 <0.001 284 <0.001 92 <0.001 189   Year * Amendment 2 0.8 0.1 0.6 0.4 0.6 0.5 0.8 0.1 0.001 10.5   Year * Block 5 0.9 0.1 0.4 1.1 0.5 0.9 0.09 2.2 0.05 2.7   Amendment * Year * Block 10 0.6 0.8 0.9 0.3 0.6 0.8 0.7 0.63 0.7 0.6 a = Soil amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.    166 Table B.3.12 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on soil amendment (bare, compost, or mulch) on Pratylenchus spp. abundance in 1 g root and 50 g bulk soil, and total nematodes in 50 g bulk soil at Site 2. Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta     Pratylenchus spp. g-1 root SD Pratylenchus spp. 50 g-1 soil SD Total Nematodes 50 g-1 soil SD Overall Bare  161 311 19 18 149 77 (n=24) Compost  116 170 12 8 209 83  Mulch  78 87 29 17 210 95          2015 Bare  239 433 16 12 135 b 92 (n=12) Compost  188 286 8 7 248 a 98  Mulch  139 132 10 4 179 ab 45          2016 Bare  83 190 21 ab 24 162 61 (n=12) Compost  44 54 15 a 8 170 67  Mulch  17 42 47 b 30 241  145  ANOVA Resultsb df P F P F P F  Amendment 2 0.3 1.3 0.003 8.3 0.03 4.1   Block 5 0.4 1.1 <0.001 10 0.01 3.7    Block * Amendment 10 0.01 3.5 0.007 3.8 0.6 0.7   Year 1 <0.001 39 <0.001 44 0.8 0.06   Year * Amendment 2 0.3 1.1 <0.001 14 0.003 7.8   Year * Block 5 0.05 2.8 0.006 4.7 0.03 2.9   Amendment * Year * Block 10 0.05 2.4 0.06 2.3 0.06 2.3 a = Soil amendments sharing the same letter within a column under the same sampling year do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.       167 Table B.3.13 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on cation exchange capacity (CEC), electrical conductivity (EC), and nutrients phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) at Site 3. Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amend-menta   CEC (meq 100 g-1 soil) SD P  (mg  kg-1) SD Ca (mg  kg-1) SD K (mg  kg-1) SD Mg  (mg  kg-1) SD Na  (mg  kg-1) SD EC (S m-1) SD Overall Bare  13.7 b 2.4 107 b 35 1995 b 342 332 b 200 311 147 22 b 2 0.5b 0.06 (n=24) Compost  17.7 a 2.9 181 a 48 2212 a 348 418 a 290 399 197 48 a 10 0.3 a 0.1  Mulch  13.3 b 2.6 105 b 39 1959 b 399 412 a 189 324 182 16 b 2 0.1 b 0.02                  2015 Bare  13.7 2.3 135  38 1813 319 423 b 137 274 60 17 2 0.1 0.09 (n=12) Compost  18.4 2.4 244  58 2081 262 1232 a 239 434 105 47 9 0.4 0.2  Mulch  13.1 2.3 134  45 1746 340 393 b 77 258 69 16 2 0.1 0.03                  2016 Bare  13.6 2.4 79  31 2176 364 332 262 347 234 26 2 0.1 0.04 (n=12) Compost  17.0 3.2 117  39 2343 435 418 342 363 290 49 10 0.3 0.08  Mulch  13.4 2.8 75  33 2172 457 412 300 389 295 15 2 0.1 0.002  ANOVA Resultsb df P F P F P F P F P F P F P F  Amendment 2 <0.001 18 <0.001 15 0.05 3.5 <0.001 30 0.3 1.4 <0.001 110 <0.001 26   Block 5 0.006 4.8 0.3 1.3 0.001 6.4 0.03 3 0.4 1.1 0.5 0.8 0.4 0.8    Block *  Amendment 10 0.9 0.3 0.8 0.5 0.4 1.1 0.3 0.8 0.5 0.9 0.3 0.9 0.4 0.9   Year 1 0.2 1.7 <0.001 153 <0.001 493 <0.001 28 0.4 0.7 0.8 0.08 0.06 4.1   Year * Amendment 2 0.1 2.5 0.1 2.5 0.4 0.9 <0.001 22 0.3 1 0.7 0.4 0.4 0.8   Year * Block 5 0.07 2.5 0.7 0.5 0.1 2.2 0.2 0.7 0.4 1.1 0.2 1.5 0.8 0.5   Amendment * Year * Block 10 0.4 1.1 0.9 0.3 0.9 0.3 0.9 0.4 0.8 0.59 0.9 0.3 0.8 0.5 a = Soil amendments sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.     168 Table B.3.14 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on on permanganate oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio, and pH at Site 3. Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta   mg POXC kg-1 soil SD TN  (%) SD TC  (%) SD OC  (%) SD OM (%) SD C: N SD pH SD Overall Bare  1313 383. 0.19 0.049 2.3 0.5 2.3 0.4 4.2 0.8 11.5 1.0 6.6 b 0.1 (n=24) Compost  1488 465 0.33 0.116 3.3 0.8 3.2 0.8 5.8 1.4 10.6 0.9 7.2 a 0.2  Mulch  1282 440 0.19 0.075 2.5 0.7 3.0 0.7 4.5 1.3 11.0 1.7 6.6 b 0.2                  2015 Bare  1786 b 622 0.21 b 0.06 2.3 b 0.5 2.4 b 0.4 3.8 b  0.6 10.7 1.2 6.5 0.2 (n=12) Compost  2040 a 751 0.43 a 0.17 3.9 a 1.0 3.8 a 1.0 6.3 a 1.7 9.3 1.4 7.2 0.1  Mulch  1753 b 702 0.22 b 0.10 2.7 b 0.9 3.8 b 0.9 4.3 ab 1.4 9.5 2.9 6.5 0.2                  2016 Bare  840 145 0.17 0.035 2.2 0.4 2.1 0.4 4.6 0.9 12.3 0.7 6.7 0.1 (n=12) Compost  936 179 0.22 0.059 2.6 0.6 2.6 0.6 5.3 1.1 11.8 0.4 7.2 0.2  Mulch  810 179 0.17 0.042 2.2 0.5 2.1 0.5 4.7 1.2 12.4 0.5 6.7 0.1  ANOVA Resultsb df P F P F P F P F P F P F P F  Amendment 2 0.2 1.8 <0.001 12 0.001 11 0.001 10 0.001 9.9 0.2 1.9 <0.001 45   Block 5 0.001 7.6 0.02 3.2 0.004 5.2 0.014 3.9 0.006 4.8 0.3 1.3 0.06 2.7    Block * Amendment 10 0.4 1.1 0.9 0.3 0.9 0.3 0.9 0.4 0.8 0.5 0.9 0.3 0.7 0.6   Year 1 <0.001 138 <0.001 46 <0.001 30 <0.001 32 0.5 0.3 <0.001 51 0.001 16   Year * Amendment 2 0.6 0.4 0.002 9.3 0.004 7.5 0.003 7.9 0.003 0.3 0.3 1.3 0.1 2.4   Year * Block 5 0.002 5.7 0.4 1.0 0.6 0.7 0.8 0.3 0.9 0.08 0.7 0.6 0.1 1.9   Amendment * Year * Block 10 0.9 0.5 0.7 0.6 0.9 0.3 0.9 0.3 0.7 0.6 0.9 0.3 0.04 2.5 a = Soil amendments sharing the same letter within a column under the same sampling year, or overall, do differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.    169 Table B.3.15 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S), and total fungi (18S), fungal-to-bacterial (F: B) ratio, and the % root colonization by arbuscular mycorrhizal fungi (AMF) at Site 3. Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values for between- and within- subject factors for each variable from the ANOVA are given. Year Amendmenta   FDA hydrolysis (µg g-1) SD log(16S copy # g-1 dry soil) SD log(18S copy # g-1 dry soil) SD F: B  Ratio SD % AMF Colonization SD Overall Bare  3.8 2.0 9.1 0.2 7.6 0.6 0.82 0.054 32.0 7.5 (n=24) Compost  4.6 3.3 9.1 0.5 7.6 0.4 0.83 0.071 24.3 8.1  Mulch  5.1 4.2 9.2 0.2 7.6 0.4 0.83 0.039 25.1 9.9              2015 Bare  1.8 b 0.1 9.5 0.2 8.4 0.5 0.87 0.056 53.0 7.1 (n=12) Compost  2.3 a 0.1 9.5 0.7 8.4 0.2 0.89 0.10 40.1 5.4  Mulch  1.8 b 0.1 9.5 0.2 8.4 0.3 0.88 0.037 42.2 11.9              2016 Bare  5.8 3.9 8.6 0.3 6.7 0.6 0.77 0.051 11.0 8.0 (n=12) Compost  6.8 6.4 8.7 0.3 6.8 0.5 0.77 0.040 8.5 10.7  Mulch  8.3 8.3 8.8 0.2 6.8 0.5 0.77 0.040 8.1 7.9  ANOVA Resultsb df P F P F P F P F P F  Amendment 2 0.5 0.7 0.9 0.1 0.9 0.02 0.9 0.07 0.07 3.1   Block 5 <0.001 8.2 0.007 4.6 <0.001 15 0.02 3.3 0.4 1.1    Block * Amendment 10 0.9 0.1 0.3 1.4 0.3 1.4 0.4 1.2 0.9 0.1   Year 1 <0.001 41 <0.001 95 <0.001 111 <0.001 112 <0.001 392   Year * Amendment 2 0.4 0.8 0.5 0.7 0.9 0.07 0.7 0.3 0.9 0.1   Year * Block 5 0.001 7.6 0.2 1.7 <0.001 23 0.001 6.9 0.4 1.02   Amendment * Year * Block 10 0.8 0.5 0.3 1.4 0.058 2.3 0.4 1.2 0.03 2.7 a = Soil amendments sharing the same letter within a column under the same sampling year, or overall, do differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.    170 Table B.3.16 ANOVA results for the effect of year (October 2015 and 2016), and interactions of year with soil amendment (bare, compost, and mulch) and block on Pratylenchus spp. abundance in 1 g root and 50 g bulk soil, and total nematodes in 50 g bulk soil at Site 3. Values represent means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Amendmenta     Pratylenchus spp. g-1 root SD Pratylenchus spp. 50 g-1 soil SD Total Nematodes 50 g-1 soil SD Overall Bare  56 b 59 28 17 276 166 (n=24) Compost  17 b 24 23 17 290 126  Mulch  147 a 388 29 22 265 141          2015 Bare  19 22 18 22 257 239 (n=12) Compost  4 5 11 9 293 115  Mulch  9 10 15 18 283 156          2016 Bare  92 95 37 13 294 93 (n=12) Compost  29 43 34 25 286 137  Mulch  284 766 43 27 246 126  ANOVA Resultsb df P F P F P F  Amendment 2 0.01 5.4 0.5 0.6 0.9 0.09   Block 5 0.01 4.1 0.08 2.3 0.1 1.8    Block * Amendment 10 0.6 0.8 0.6 0.7 0.5 0.9   Year 1 <0.001 25 <0.001 38 0.8 0.04   Year * Amendment 2 0.5 0.8 0.8 0.3 0.5 0.5   Year * Block 5 0.01 4.1 0.9 0.2 0.6 0.6   Amendment * Year * Block 10 0.5 0.9 0.6 0.7 0.9 0.3 a = Soil amendments sharing the same letter within a column under the same sampling year, or overall, do differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.      171  Table B.3.17 Effect of soil treatment (fumigation, compost, or legacy effects) on cation exchange capacity (CEC), electrical conductivity (EC), and nutrients phosphorus (P), calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na) at Site 4 in October 2015 and 2016 sampling years. Values represent main factor means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Treatmenta CEC (meq 100 g-1 soil) SD P  (mg  kg-1) SD Ca (mg  kg-1) SD K (mg  kg-1) SD Mg  (mg  kg-1) SD Na  (mg  kg-1) SD EC (S m-1) SD Overall  Fumigation Effects (n=72)                Fumigation  8.9 1.6 97 40 1321 234 233 78 163 27 43 13 0.1 0.07  No Fumigation  9.4 1.7 91 62 1370 247 241 70 172 39 33 7 0.1 0.05  Compost Effects (n=72)                Compost  9.5 1.6 97 57 1389 235 261 63 181 34 37 9 0.1 0.06  No Compost  8.8 1.6 91 48 1302 243 213 78 155 29 39 13 0.1 0.08  Legacy Effects (n=48)                Mulch 9.5 1.7 84 b 45 1370 263 250 82 170 35 39 11 0.1 0.8  No treatment 8.9 1.6 84 b 49 1319 221 230 71 164 31 36 13 0.1 0.07  Phosphorus fertigation 9.2 1.7 114 a 58 1348 246 231 69 169 37 39 11 0.1 0.07 2015 Fumigation Effects (n=36)                Fumigation  8.9 1.4 117                        53 1250 189 260 88 162 23 31 6 0.1 0.05  No Fumigation  9.1 1.5 114 94 1231 194 254 68 166 30 26 3 0.1 0.08  Compost Effects (n=36)                Compost  9.2 1.5 121 85 1259 190 282 69 174 25 27 4 0.08 0.04  No Compost  8.8 1.4 109 67 1222 192 232 81 154 25 30 6 0.1 0.09  Legacy Effects (n=24)                Mulch 9.2 1.3 106 64 1245 208 265 87 162 25 29 5 0.1 0.06  No treatment 8.7 1.5 104 73 1225 179 256 79 161 26 27 5 0.09 0.04  Phosphorus fertigation 9.0 1.5 137 88 1252 190 250 71 168 30 30 6 0.01 0.09                  172 Year Treatmenta CEC (meq 100 g-1 soil) SD P  (mg  kg-1) SD Ca (mg  kg-1) SD K (mg  kg-1) SD Mg  (mg  kg-1) SD Na  (mg  kg-1) SD EC (S m-1) SD  2016 Fumigation Effects (n=36)                Fumigation  8.9 b 1.7 77 27 1391 b 278 205 b 68 164 31 53.9 a 20.5 0.2 a 0.1  No Fumigation  9.7 a 1.9 67 30 1509 a 300 228 a 71 178 48 39.6 b 11.7 0.1 b 0.03  Compost Effects (n=36)                Compost  9.8 a 1.7 73 29 1519 a 279 239 57 187 a 42 45.2 14.9 0.1 0.08  No Compost  8.8 b 1.8 72 30 1381 b 294 193 75 155 b 34 48.3 20.9 0.1 0.08  Legacy Effects (n=24)                Mulch 9.7 2.0 62 26 1494 318 235 78 178 44 48.5 17.2 0.1 0.1  No treatment 9.1 1.7 64 26 1413 263 203 63 166 36 44.6 21.4 0.1 0.09  Phosphorus fertigation 9.3 1.9 91  28 1443 303 212 67 169 44 47.2 15.7 0.1 0.04 ANOVA Resultsb P F P F P F P F P F P F P F  Fumigation (df=1) 0.06 3.7 0.4 0.6 0.1 2.5 0.5 0.5 0.5 0.5 0.1 2.7 0.43 0.6  Compost (df=1) 0.01 7.1 0.5 0.3 0.03 5.1 <0.001 16 <0.001 16 <0.001 19.4 0.56 0.3  Legacy (df=2) 0.2 1.5 0.02 5.4 0.5 0.5 0.3 1.3 0.3 1.3 0.7 0.4 0.563 0.5  Block (df=5) <0.001 15 0.001 5.2 <0.001 13 <0.001 12 <0.001 12 <0.001 8.5 <0.001 4.1  Fumigation * Compost (df=1) 0.5 0.3 0.5 0.4 0.56 0.3 0.1 1.8 0.7 0.1 0.8 0.05 0.54 0.4  Fumigation * Legacy  (df=2) 0.9 0.06 0.8 0.1 0.5 0.6 0.5 0.6 0.2 1.5 0.9 0.08 0.86 0.1  Fumigation * Block (df=5) 0.022 2.9 <0.001 5.7 0.003 4.1 0.1 1.8 <0.001 5.4 0.2 1.7 0.7 0.1  Compost * Legacy (df=2) 0.8 0.1 0.9 0.06 0.2 1.3 0.2 1.5 0.2 1.5 0.9 0.08 0.2 1.5  Compost * Block (df=5) 0.2 1.3 0.1 1.7 0.06 2.3 0.09 2.1 0.8 0.4 0.2 1.6 0.037 2.6                  173  ANOVA Resultsb P F P F P F P F P F P F P F  Legacy * Block (df=10) 0.05 2.1 0.9 0.2 0.6 0.8 0.6 0.9 0.2 1.4 0.5 0.9 0.5 0.8  Fumigation * Compost *  Legacy (df=2) 0.1 1.9 0.06 2.9 0.06 2.9 0.7 0.3 0.5 0.5 0.1 2.2 0.1 2.5  Fumigation * Compost * Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.2 1.4 0.5 0.8 0.5 0.8  Fumigation * Legacy * Block (df=10) 0.8 0.6 0.8 0.6 0.8 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2  Compost * Legacy * Block (df=10) 0.06 2.1 0.9 0.2 0.6 0.8 0.6 0.9 0.2 1.4 0.5 0.9 0.5 0.8   Fumigation * Compost * Legacy * Block (df=10) 0.8 0.6 0.9 0.2 0.8 0.06 2.1 0.9 0.2 0.6 0.8 0.6 0.9 0.2  Year (df=1) 0.03 6.4 <0.001 34 <0.001 71 <0.001 55 0.02 5.3 <0.001 91 0.00 7.7  Year * Fumigation (df=1) 0.04 4.2 0.5 0.4 0.003 9.4 0.021 5.6 0.1 2.6 0.005 8.4 0.01 6.9  Year * Compost (df=1) 0.01 6.8 0.4 0.5 0.04 4.2 0.7 0.1 0.05 4 0.06 4 0.5 0.3  Year * Legacy (df=2) 0.8 0.1 0.9 0.06 0.5 0.6 0.2 1.5 0.2 1.5 0.2 1.8 0.2 1.5  Year * Block (df=5) 0.001 5.5 0.025 2.9 <0.001 6.3 0.09 2.1 0.002 4.9 0.001 5.2 0.03 2.8  Year * Fumigation  *  Compost (df=1) 0.2 1.5 0.8 0.06 0.2 1.6 0.2 1.6 0.2 1.5 0.8 0.06 0.2 1.6  Year * Fumigation  *  Legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.8 0.1  Year * Fumigation *  Block (df=5) 0.01 2.9 <0.001 5.3 0.05 2.4 0.5 0.8 0.5 0.8 0.4 1.0 0.6 0.6  Year * Compost  *  Legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.8 0.1  Year * Compost  *  Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.4 1.0 0.6 0.6 0.5 0.8 0.7 0.5   174  ANOVA Resultsb P F P F P F P F P F P F P F  Year * Legacy  *  Block (df=10) 0.05 2.1 0.6 0.7 0.9 0.4 0.03 2.3 0.8 0.6 0.9 0.2 0.8 0.6  Year * Fumigation  *  Compost  *  Legacy (df=2) 0.1 1.9 0.06 2.9 0.8 0.1 0.06 2.9 0.1 2.2 0.1 2.5 0.1 2.2  Year * Fumigation  *  Compost  *  Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.4 1.0 0.6 0.6 0.5 0.8 0.7 0.5  Year * Fumigation  *  Legacy  *  Block (df=10) 0.6 0.7 0.9 0.4 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2  Year * Compost  *  Legacy  *  Block (df=10) 0.9 0.2 0.9 0.2 0.8 0.6 0.2 1.4 0.4 0.9 0.8 0.6 0.8 0.6  Year * Fumigation  *  Compost  *  Legacy  *  Block (df=10) 0.4 0.9 0.8 0.6 0.9 0.3 0.8 0.6 0.8 0.6 0.9 0.2 0.8 0.6 a = Fumigation effects, compost or legacy effect means not sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.                 175 Table B.3.18 Effect of soil treatment (bare, compost, or mulch) on permanganate oxidizable carbon (POXC) content, total nitrogen (TN), total carbon (TC), organic carbon (OC), organic matter (OM), carbon to nitrogen (C: N) ratio and pH at Site 4 in October 2015 and 2016 sampling years. Values represent main factor means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Treatmenta mg POXC kg-1 soil SD TN (%) SD TC  (%) SD OC  (%) SD OM  (%) SD C: N SD pH SD Overall  Fumigation Effects (n=72)                Fumigation  449 135 0.09 0.01 1.3 0.2 1.2 0.3 2.5 0.5 14.3 2.6 7.1 0.2  No Fumigation  516 176 0.09 0.01 1.4 0.4 1.3 0.5 2.7 0.8 15.2 3.4 7.2 0.2  Compost Effects (n=72)                Compost  546 a 117 0.1 0.02 1.4 0.3 1.5 a 0.4 2.9 a 0.7 15.1 3.3 7.5 a  0.2  No Compost  419 b 106 0.08  0.01 1.3 0.2 1.1 b 0.3 2.2 b 0.6 14.4 2.8 7.2 b 0.2  Legacy Effects (n=48)                Mulch 505 a 159 0.09 0.02 1.4 0.4 1.6 a 0.5 2.9 a 0.5 15.8 3.9 7.2 0.1  No treatment 486 ab 135 0.09 0.02 1.3 0.2 1.0 b 0.2 2.1 b 0.5 14.1 2.6 7.3 0.2  Phosphorus fertigation 387 b 111 0.08 0.02 1.0 0.3 1.0 b 0.3 2.7 a 0.6 13.4 1.0 7.3 0.3 2015 Fumigation Effects (n=36)                Fumigation  507  165 0.1 0.01 1.5 0.2 1.4 0.3 2.3 0.5 14.7 3.7 7.0 0.2  No Fumigation  592   228 0.1 0.01 1.6 0.5 1.5 0.6 2.5 1.1 15.8 5.2 7.0 0.2  Compost Effects (n=36)                Compost  612  128 0.1 0.02 1.6 0.5 1.6 0.5 2.6 0.9 15.5 5.2 7.1 0.1  No Compost  488   122 0.1 0.006 1.6 0.2 1.3 0.4 2.2 0.6 14.9 3.8 7.0 0.2  Legacy Effects (n=24)                Mulch 528 230 0.1 0.01 1.5 0.2 1.6 0.4 2.6 0.6 15.5 2.2 6.9 0.1  No treatment 619 200 0.1 0.02 1.6 0.6 1.4 0.7 2.4 1.2 15.7 6.5 7.1 0.2  Phosphorus fertigation 504 160 0.1 0.02 1.5 0.2 1.4 0.2 2.2 0.4 14.5 4.1 7.0 0.2 2016 Fumigation Effects (n=36)                Fumigation  390  106 0.07 0.02 1.0 0.2 0.9 0.2 2.6 0.6 13.9 1.5 7.2 0.2  No Fumigation  440   125 0.07 0.02 1.1 0.3 1.1 0.3 2.9 0.7 14.6 1.6 7.3 0.2  Compost Effects (n=36)                Compost  480  106 0.08 a 0.01 1.2 a 0.2 1.2 0.2 2.9 0.6 14.6 1.4 7.3 0.2  No Compost  350  91 0.06 b 0.01 0.9 b 0.2 0.8 0.2 2.5 0.6 13.9 1.7 7.3 0.2   176 Year Treatmenta mg POXC kg-1 soil SD TN (%) SD TC  (%) SD OC  (%) SD OM  (%) SD C: N SD pH SD 2016 Legacy Effects (n=24)                Mulch 390 118 0.079 0.021 1.2 0.2 1.2 0.2 3.0 0.8 15.8 a 1.4 7.2 0.1  No treatment 468 110 0.073 0.018 0.9 0.2 0.9 0.2 2.5 0.5 13.6 b 1.2 7.5 0.1  Phosphorus fertigation 387 111 0.076 0.021 1.0 0.3 0.9 0.3 2.7 0.6 13.4 b 1.0 7.3 0.2 ANOVA Resultsb P F P F P F P F P F P F P F  Fumigation (df=1) <0.001 12.839 0.15 2.17 0.1 2.29 0.1 2.29 0.091 2.9 0.26 1.3 0.3 1.4  Compost (df=1) <0.001 12.8 <0.001 15.1 0.003 10.2 <0.001 24 <0.001 15.8 0.47 0.53 0.016 12.6  Legacy (df=2) 0.02 5.4 0.32 1.2 0.16 1.96 0.014 4.9 0.033 3.7 0.11 2.3 0.12 25  Block (df=5) 0.002 4.8 0.06 2.4 0.06 2.4 <0.001 9.1 <0.001 11.7 0.4 0.9 0.39 0.9  Fumigation * Compost (df=1) 0.6 0.16 0.15 2.17 0.7 0.13 0.7 0.13 0.9 0.001 0.55 0.37 0.6 0.4  Fumigation * Legacy (df=2) 0.8 0.18 0.068 2.9 0.16 1.96 0.7 0.36 0.5 0.59 0.13 2.2 0.009 7.8  Fumigation * Block (df=5) 0.7 0.58 0.49 0.91 0.18 1.62 0.4 1.03 0.67 0.64 0.54 0.83 0.5 0.8  Compost * Legacy (df=2) 0.1 1.96 0.068 2.9 0.7 0.36 0.7 0.36 0.5 0.59 0.13 2.2 0.14 2.5  Compost * Block (df=5) 0.7 0.58 0.49 0.91 0.18 1.62 0.18 1.62 0.26 1.4 0.54 0.83 0.5 0.8  Legacy * Block (df=10) 0.6 0.74 0.92 0.44 0.9 0.26 0.9 0.26 0.99 0.26 0.8 0.6 0.99 0.26  Fumigation * Compost * Legacy (df=2) 0.8 0.15 0.92 0.06 0.28 1.3 0.2 1.5 0.2 1.5 0.9 0.08 0.007 6.2  Fumigation * Compost * Block (df=5) 1.3 0.19 1.7 0.067 2.3 0.095 2.1 0.8 0.4 0.2 1.6 1.3 1.3 0.19  Fumigation * Legacy h * Block (df=10) 0.9 0.25 0.6 0.8 0.6 0.9 0.2 1.4 0.5 0.9 0.5 0.8 0.99 0.25                                 177  ANOVA Resultsb P F P F P F P F P F P F P F  Compost * Legacy * Block (df=10) 0.2 1.4 0.5 0.9 0.5 0.8 0.9 0.2 0.2 1.4 0.5 0.9 0.5 0.8   Fumigation * Compost * Legacy * Block (df=10) 0.2 1.4 0.5 0.9 0.5 0.8 0.9 0.2 0.2 1.4 0.5 0.9 0.08 4.6  Year (df=1) <0.001 34 <0.001 145 <0.001 83 0.02 73 <0.001 18 <0.001 5.4 <0.001 158  Year * Fumigation (df=1) 0.381 0.7 0.8 0.03 0.8 0.06 0.8 0.06 0.8 0.05 0.8 0.02 0.8 0.026  Year * Compost (df=1) 0.003 10 0.003 10.1 0.01 0.7 0.7 0.1 0.9 0.001 0.5 0.3 0.6 0.3  Year * Legacy (df=2) 0.16 1.9 0.06 2.9 0.7 0.3 0.7 0.3 0.5 0.5 0.1 2.2 0.1 2.5  Year * Block (df=5) <0.001 6.2 <0.001 6.2 0.1 1.6 0.1 1.6 0.6 0.6 0.5 0.8 0.5 0.8  Year * Fumigation  *   Compost (df=1) 0.6 0.1 0.1 2.17 0.7 0.1 0.7 0.1 0.9 0.001 0.5 0.3 0.6 0.4  Year * Fumigation  *  Legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.5 0.5  Year * Fumigation  *  Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.4 1.0 0.6 0.6 0.5 0.8 0.5 0.8  Year * Compost  *  Legacy (df=2) 0.1 1.9 0.06 2.9 0.7 0.3 0.7 0.3 0.5 0.5 0.1 2.2 0.1 2.5  Year * Compost  *  Block (df=5) 0.7 0.5 0.002 4.8 0.1 1.6 0.1 1.6 0.2 1.4 0.5 0.8 0.5 0.8  Year * Legacy  *  Block (df=10) 0.6 0.7 0.9 0.4 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2  Year * Fumigation  *  Compost  *  Legacy (df=2) 0.7 0.3 0.7 0.3 0.7 0.3 0.7 0.3 0.5 0.5 0.1 2.2 0.14 2.5  Year * Fumigation  *  Compost  *  Block (df=5) 0.2 1.3 0.1 1.7 0.06 2.3 0.09 2.1 0.8 0.4 0.2 1.6 0.2 1.6  Year * Fumigation  *  Legacy  *  Block (df=10) 0.9 0.2 0.6 0.8 0.6 0.8 0.6 0.9 0.2 1.4 0.5 0.9 0.5 0.8   178  ANOVA Resultsb P F P F P F P F P F P F P F  Year * Compost  *  Legacy *  Block (df=10) 0.6 0.8 0.6 0.8 0.5 0.9 0.5 0.8 0.2 1.6 0.03 2.6 0.2 1.6  Year * Fumigation  *  Compost  *  Legacy  *  Block (df=10) 0.5 0.9 0.5 0.8 0.9 0.2 0.6 0.8 0.6 0.9 0.2 1.4 0.9 0.2 a = Fumigation effects, compost or legacy effect means not sharing the same letter within a column under the same sampling year, or overall, do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level.                            179  Table B.3.19 Effect of soil treatment (bare, compost, or mulch) on fluorescein diacetate (FDA) hydrolysis, abundance of total bacteria (16S copy number) and total fungi (18S copy number), fungal-to-bacterial (F: B) ratio, % root colonization by arbuscular mycorrhizal fungi (AMF), and Pratylenchus spp. per one-gram root and 50-grams soil at Site 4 in October 2015 and 2016 sampling years. Values represent main factor means (overall and for each sampling year) and standard deviations (SD). Degrees of freedom (df), and P-and F-values from the ANOVA are given. Year Treatmenta FDA hydrolysis (µg g-1) SD Log (16S copy # g-1 dry soil) SD Log (18S copy # g-1 dry soil) SD F: B ratio SD % AMF Coloniz-ation SD Praty- lenchus spp. 100 g-1 soilc SD Praty- lenchus spp. g-1 dry rootc SD Overall  Fumigation Effects (n=72)                Fumigation  1.8 1.4 8.8 0.4 7.2 0.5 0.82 0.093 34.9 17.6 50 75 130 b 252  No Fumigation  2.6 1.7 8.9 0.5 7.5 0.5 0.84 0.083 36.8 16.9 52 42 381 a 408  Compost Effects (n=72)                Compost  2.4 1.8 8.9 0.5 7.4 0.5 0.84 0.071 37.3 17.2 47 74 156 b 226  No Compost  2.1 1.5 8.8 0.6 7.3 0.5 0.83 0.097 34.1 16.8 50 49 354 a 442  Legacy Effects (n=48)                Mulch                No treatment 2.0 1.6 9.0 0.3 7.4 0.6 0.82 0.068 36.8 17.6 49 62 204 270  Phosphorus fertigation 2.5 1.7 8.7 0.7 7.3 0.5 0.84 0.12 35.8 16.9 49 81 303 449 2015 Fumigation Effects (n=36)                Fumigation  1.9 b 1.4 9.0 b 0.3 7.6 b 0.5 0.85 0.074 49.1 17.9 2  4 139 305  No Fumigation  2.5 a 2.4 9.4 a 0.3 8.1 a 0.3 0.86 0.048 51.0 17.9 10  1 313 379  Compost Effects (n=36)                Compost  2.4 2.3 9.2 0.6 8.1 a 0.4 0.88 a 0.069 54.2 18.0 2 3 150 197  No Compost  2.1 1.7 9.1 0.4 7.7 b 0.5 0.84 b 0.051 45.2 17.1 2 15 301 449                   180 Year Treatmenta FDA hydrolysis (µg g-1) SD Log (16S copy # g-1 dry soil) SD Log (18S copy # g-1 dry soil) SD F: B ratio SD % AMF Coloniz-ation SD Praty- lenchus spp. 100 g-1 soilc SD Praty- lenchus spp. g-1 dry rootd SD 2015 Legacy Effects (n=24)                Mulch 1.8 2.2 9.3 0.4 7.9 0.5 0.85 0.062 52.4 16.9 6 12 176 245  No treatment 2.8 2.1 9.1 0.6 7.8 0.5 0.86 0.070 47.9 17.5 5 10 303 508  Phosphorus fertigation 2.1 1.8 9.2 0.4 7.9 0.5 0.86 0.057 49.9 19.3 6 12 197 237 2016 Fumigation Effects (n=36)                Fumigation  1.7 1.4 8.5 0.5 6.7 0.4 0.79 0.11 20.6 17.3 97 145 120 199  No Fumigation  2.6 1.1 8.3 0.6 6.8 0.7 0.82 0.11 22.5 16.0 93 84 448 436  Compost Effects (n=36)                Compost  2.4 1.3 8.5 0.4 6.6 0.5 0.79 0.073 20.3 16.5 92 146 161 255  No Compost  2.0 1.3 8.4 0.7 6.8 0.6 0.82 0.14 22.9 16.7 98 83 407 435  Legacy Effects (n=24)                Mulch 2.2 1.1 8.6 0.2 6.8 0.7 0.78 0.073 21.1 18.3 92 112 232 295  No treatment 2.2 1.3 8.2 0.8 6.7 0.5 0.82 0.14 23.7 16.4 92 152 303 390  Phosphorus fertigation 2.1 1.7 8.4 0.6 6.7 0.5 0.81 0.11 19.9 15.4 101 84 318 437 ANOVA Resultsb P F P F P F P F P F P F P F  Fumigation (df=1) 0.5 0.3 0.3 0.8 <0.001 21.1 0.2 1.4 0.2 1.5 0.05 3.7 <0.001 14.7  Compost (df=1) 0.8 0.05 0.1 1.7 <0.001 17.5 0.8 0.05 0.09 2.1 0.05 4.0 0.007 7.8  Legacy (df=2) 0.9 0.06 0.8 0.4 0.9 0.06 0.5 0.5 0.1 2.2 0.5 0.6 0.9 0.06 Block (df=5) 0.1 1.7 0.2 1.1 <0.001 6.0 0.1 1.9 0.2 1.2 0.002 4.7 0.09 1.9 Fumigation * Compost (df=1) 0.2 1.5 0.8 0.06 <0.001 6.0 0.2 1.4 0.6 0.2 0.2 1.6 0.2 1.6   181  ANOVA Resultsb P F P F P F P F P F P F P F  Fumigation * Legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.009 7.8  Fumigation * Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.4 1.0 0.6 0.6 0.5 0.8 0.5 0.8  Compost * Legacy (df=2) 0.1 1.9 0.06 2.9 <0.001 4.0 0.7 0.3 0.5 0.5 0.1 2.2 0.1 2.5  Compost * Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.2 1.4 0.5 0.8 0.5 0.8  Legacy * Block (df=10) 0.6 0.7 0.9 0.4 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2  Fumigation * Compost * Legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.1 2.2  Fumigation * Compost * Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.4 1.0 0.6 0.6 0.5 0.8 0.5 0.8  Fumigation * Legacy * Block (df=10) 0.8 0.6 0.9 0.2 0.8 0.6 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2  Compost * Mulch * Block (df=10) 0.9 0.2 0.8 0.6 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2 0.9 0.2   Fumigation * Compost * Legacy * Block (df=10) 0.8 0.6 0.9 0.2 0.8 0.6 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2  Year (df=1) 0.8 0.02 <0.001 52 <0.001 274 <0.001 12 <0.001 71 <0.001 245 0.3 1.1  Year * Fumigation (df=1) <0.001 6.3 0.008 7.4 <0.001 10 0.7 0.1 0.6 0.2 0.2 1.3 0.1 2.3  Year * Compost (df=1) 0.2 1.5 0.8 0.06 <0.001 13 <0.001 6.1 0.7 0.07 0.8 0.02 0.2 1.6  Year * Legacy (df=2) 0.9 0.06 0.8 0.4 0.8 0.4 0.7 0.3 0.1 2.2 0.5 0.5 0.1 2.2  Year * Block (df=5) 0.01 3.5 0.2 1.1 <0.001 21 <0.001 6.6 0.3 1.0 0.013 3.3 0.4 1.0  Year * Fumigation  *  Compost (df=1) 0.2 1.5 0.8 0.06 0.2 1.5 0.8 0.06 0.2 1.5 0.8 0.06 0.2 1.5   182  ANOVA Resultsb P F P F P F P F P F P F P F  Year * Fumigation  *  Legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.8 0.18  Year * Fumigation  *  Block (df=5) 0.5 0.8 0.7 0.5 0.01 2.9 0.7 0.5 0.4 0.9 0.1 1.6 0.4 1.0  Year * Compost  *  Legacy (df=2) 0.9 0.06 0.5 0.5 0.1 2.2 0.5 0.6 0.9 0.06 0.9 0.06 0.5 0.5  Year * Compost  *  Block (df=5) 0.1 1.9 0.2 1.2 0.002 4.7 0.09 1.9 0.1 1.9 0.04 2.5 0.1 1.9  Year * Legacy  *  Block (df=10) 0.8 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2 0.8 0.2 0.9 0.2  Year * Fumigation  *  Compost  *  Legacy (df=2) 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.1 1.9 0.7 0.3 0.5 0.5  Year * Fumigation  *  Compost  *  Block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.2 1.4 0.5 0.8 0.5 0.8  Year * Fumigation  *  Legacy  *  Block (df=10) 0.8 0.6 0.8 0.6 0.8 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2  Year * Compost  *  Legacy  *  Block (df=10) 0.6 0.8 0.6 0.8 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2 0.6  Year * Fumigation  *  Compost  *  Legacy  *  Block (df=10) 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 a = Fumigation effects, compost or legacy effect means not sharing the same letter within a column under the same sampling year do not differ significantly (P>0.05), according to Tukey’s HSD test.   b = Values are significant at a P ≤ 0.05 significance level. c = Data courtesy of Dr. Thomas Forge in October 2016 sampling year.      183 Table B.3.20 Two-way, three-way, four-way, and five-way ANOVA interactions of soil treatment (fumigation effects, compost effects, or legacy effects) and/ or sterilization regime (sterilized or non-sterilized) on measures of plant growth (total root length, root surface area, root weight, shoot weight, plant weight, and shoot height) for plants grown in soil from Site 4. Values represent main factor means and standard deviations (SD). Degrees of freedom (df), and P-and F-values for each variable from the ANOVA are given.  Plant growth measurements Treatmenta root length (cm) SD root surface area (cm2) SD root weight (g) SD shoot weight (g) SD shoot height (cm) SD plant biomass (g) SD Fumigation Effects (n=72)             Fumigation  968 34 44.4 b 21.22 0.66 b 0.38 0.64 b 0.38 10.9 2.4 1.2 b 0.6 No Fumigation  977  35 54.4 a 22.0 0.96 a 0.39 0.82 a 0.40 11.3 2.5 1.9 a 0.6 Compost Effects (n=72)             Compost  987 a 34 46.5 21.6 0.76 0.39 0.69 0.39 11.1 2.5 1.4 0.6 No Compost  940 b 34 50.5 21.6 0.79 0.39 0.75 0.39 11.4 2.5 1.6 0.6 Legacy Effects (n=48)             Mulch 945 34 40.9 21.6 0.64 0.38 0.66 0.38 11.1 3.1 1.3 0.7 No treatment 986 35 52.8 21.6 0.85 0.38 0.74 0.38 11.2 3.1 1.6 0.7 Phosphorus fertigation 959 34 51.7 21.6 0.84 0.38 0.76 0.38 11.4 3.1 1.6 0.7 Sterilization Effects (n=72)             Sterilization 991 37 56.5 a 23.2 1.11 a 0.41 0.79 a 0.42 11.1 2.7 1.9 a 0.6 No Sterilization 941 32 41.8 b 20.3 0.57 b 0.36 0.66 b 0.36 11.5 2.3 1.2 b 0.5 ANOVA Resultsb P F P F P F P F P F P F sterilization * fumigation (df=1) 0.2 1.5 0.8 0.06 0.8 0.06 0.8 0.06 0.7 0.07 0.8 0.02 sterilization * compost (df=1) 0.7 0.07 0.8 0.02 0.7 0.07 0.8 0.02 0.7 0.07 0.8 0.02 sterilization * legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 sterilization * block (df=5) 0.4 0.9 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.2 1.4 fumigation * compost (df=1) 0.7 0.07 0.8 0.02 0.7 1.5 0.8 0.06 0.8 0.06 0.8 0.06 fumigation * legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 fumigation * block (df=5) 0.8 0.6 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2 compost * legacy (df=2) 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.1 1.9 0.7 0.3 compost * block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.2 1.4 0.5 0.8 legacy * block (df=10) 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2 0.9 0.2 sterilization * fumigation * compost (df=1) 0.2 1.5 0.8 0.06 0.8 0.06 0.8 0.06 0.2 1.5 0.8 0.06 sterilization * fumigation * legacy (df=2) 0.8 0.1 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 sterilization * fumigation * block (df=3) 0.1 1.9 0.7 0.3 0.1 1.9 0.06 2.9 0.1 1.9 0.7 0.3 sterilization * compost * legacy (df=2) 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.06 2.9 sterilization * compost * block df=5) 0.8 0.6 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2 sterilization * legacy * block (df=10) 0.8 0.6 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2 fumigation * compost * legacy (df=2) 0.06 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.06 2.9 fumigation * compost * block (df=5) 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.2 1.4 0.5 0.8                184 ANOVA Resultsb P F P F P F P F P F P F fumigation * legacy * block (df=10) 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2 0.9 0.2 0.9 0.2  compost * legacy * block (df=10) 0.8 0.6 0.9 0.2 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2              sterilization * fumigation * compost * legacy (df=2) 0.068 2.9 0.1 1.9 0.7 0.3 0.5 0.5 0.1 2.2 0.06 2.9  sterilization * fumigation * compost * block (df=3) 0.1 1.9 0.2 1.2 0.2 1.2 0.09 1.9 0.1 1.9 0.1 1.9  sterilization * fumigation * legacy * block (df=6) 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.2 1.4 0.5 0.8  sterilization * compost * legacy * block (df=10) 0.9 0.2 0.9 0.2 0.8 0.6 0.9 0.2 0.9 0.2 0.8 0.6  fumigation * compost * legacy * block (df=10) 0.8 0.6 0.9 0.2 0.9 0.2 0.9 0.2 0.9 0.2 0.9 0.2  sterilization * fumigation * compost * legacy * block (df=6) 0.7 0.5 0.4 0.9 0.1 1.6 0.1 1.6 0.2 1.4 0.5 0.8 a = Results are significant at a P≤0.05 significance level.   

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