Arts and Sciences, Irving K. Barber School of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Gebretsadikan, Tirhas Mebratu
Master of Science - MSc
University of British Columbia
In the Okanagan Valley, sweet cherry production has expanded to higher altitudes due to climate change, but water availability and maintenance of soil health are major concerns for sustainable production. This study examined the effects of mulches and postharvest deficit irrigation (PDI) on soil health, tree productivity and fruit yield and quality in cherry orchards over two seasons. Sweet cherry trees were planted with six replicates at three sites in a randomized block split-plot design, while a split-split-plot experimental design was used at the fourth site. Two irrigation rates (100% and ~70% water supply) were the main-plots, with mulches (compost, woodchips and bare ground) as sub-plots for three sites. Fumigation, compost and legacy effect of mulch and P fertigation were assigned accordingly as main-, sub- and sub-subplots at Site 4. Mulched (Sites 1-3) or incorporated (Site 4) compost increased soil organic matter, nutrients, pH and conductivity at all sites. Compost decreased arbuscular mycorrhizal fungi (AMF) colonization and increased total bacteria in soil, while fewer root P. penetrans were detected from incorporated compost alone or in combination with fumigation compared to the corresponding control. By contrast, woodchips affected only a few of these soil properties, including increasing soil C and C:N, and decreasing AMF colonization. The legacy effect of mulch at Site 4 was associated with increases in most soil nutrients, and higher total fungal populations when legacy mulch was combined with fumigation. Compost increased soil moisture compared to bare ground, but had no effect on stem water potential of cherry trees. Compost also increased leaf area, a few foliar nutrients and fruit quality attributes, while no impacts of woodchips were observed on these parameters compared to bare soil, with very few exceptions. Use of PDI saved up to 28% irrigation water after fruit harvest per season without affecting soil moisture, soil biochemical properties, stem water potential, or any tree growth and productivity parameters. Overall, results suggest that mulches are a promising strategy to maintain soil moisture, improve soil fertility, plant growth and productivity, and PDI has the potential to reduce irrigation water use after harvest without affecting cherry production.
IMPACT OF POSTHARVEST DEFICIT IRRIGATION AND ORGANIC AMENDMENTS ON SWEET CHERRY PRODUCTION AT NEWLY AND PREVIOUSLY ESTABLISHED ORCHARDS IN THE OKANAGAN VALLEY by Tirhas Mebratu Gebretsadikan B.Sc., Jimma University, 2010 MSc., Jimma University, 2013 MSc., Hebrew University of Jerusalem, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2019 © Tirhas Mebratu Gebretsadikan, 2019 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: IMPACT OF POSTHARVEST DEFICIT IRRIGATION AND ORGANIC AMENDMENTS ON SWEET CHERRY PRODUCTION AT NEWLY AND PREVIOUSLY ESTABLISHED ORCHARDS IN THE OKANAGAN VALLEY submitted by Tirhas Mebratu Gebretsadikan in partial fulfillment of the requirements of the degree of Master of Science Dr. Melanie Jones, Biology, Irving K. Barber School of Arts and Sciences Supervisor Dr. Louise Nelson, Biology, Irving K. Barber School of Arts and Sciences Co-Supervisor Dr. Thomas Forge, Soil Ecology, Agriculture and Agri-food Canada Supervisory Committee Member Dr. Christopher Chanway, Forest and Conservation Science, Faculty of Forestry University Examiner iii Abstract In the Okanagan Valley, sweet cherry production has expanded to higher altitudes due to climate change, but water availability and maintenance of soil health are major concerns for sustainable production. This study examined the effects of mulches and postharvest deficit irrigation (PDI) on soil health, tree productivity and fruit yield and quality in cherry orchards over two seasons. Sweet cherry trees were planted with six replicates at three sites in a randomized block split-plot design, while a split-split-plot experimental design was used at the fourth site. Two irrigation rates (100% and ~70% water supply) were the main-plots, with mulches (compost, woodchips and bare ground) as sub-plots for three sites. Fumigation, compost and legacy effect of mulch and P fertigation were assigned accordingly as main-, sub- and sub-sub-plots at Site 4. Mulched (Sites 1-3) or incorporated (Site 4) compost increased soil organic matter, nutrients, pH and conductivity at all sites. Compost decreased arbuscular mycorrhizal fungi (AMF) colonization and increased total bacteria in soil, while fewer root P. penetrans were detected from incorporated compost alone or in combination with fumigation compared to the corresponding control. By contrast, woodchips affected only a few of these soil properties, including increasing soil C and C:N, and decreasing AMF colonization. The legacy effect of mulch at Site 4 was associated with increases in most soil nutrients, and higher total fungal populations when legacy mulch was combined with fumigation. Compost increased soil moisture compared to bare ground, but had no effect on stem water potential of cherry trees. Compost also increased leaf area, a few foliar nutrients and fruit quality attributes, while no impacts of woodchips were observed on these parameters compared to bare soil, with very few exceptions. Use of PDI saved up to 28% irrigation water after fruit harvest per season without affecting soil moisture, soil biochemical properties, stem water potential, or any tree growth and productivity parameters. Overall, results suggest that mulches are a promising strategy to maintain soil moisture, improve soil fertility, plant growth and productivity, and PDI has the potential to reduce irrigation water use after harvest without affecting cherry production. iv Lay Summary Using field experiments at cherry orchards in the Okanagan Valley over two seasons, we examined how reduced irrigation supply after harvest and soil amendment using yard waste compost and woodchips affected cherry tree growth, productivity, and water stress, and soil biotic and abiotic properties. Compost mulch positively affected soil moisture and fertility, and some cherry foliar nutrients and fruit quality attributes when compared to bare-ground control plots. Despite its relatively lower impact than compost, application of woodchips resulted in higher concentrations of some soil nutrients and a few foliar nutrients relative to non-amended soils. However, woodchips had very limited effects on soil biotic properties, tree growth and water stress after two seasons. The use of postharvest deficit irrigation saved up to 28% of irrigation water without affecting soil biochemical properties and cherry production as compared to full irrigation. v Preface Mulches and irrigation application strategy trials were established at four sweet cherry orchards in the Okanagan Valley. Drs. Louise Nelson, Melanie Jones and Kirsten Hannam (currently at Summerland Research and Development Center (SuRDC) from UBCO and Drs. Thomas Forge and Denise Neilsen from SuRDC were responsible for site selection and experimental design at these orchards. Coral Beach Farms Ltd. owned and maintained the two cherry orchards located in Coldstream, BC, and Lavington, BC, while Dendy Orchards owned and maintained the third orchard located in Kelowna, BC. The fourth cherry orchard was located at SuRDC in Summerland, BC and maintained by its employees. I performed the field work, sampling, processing and analysis of samples for 2016 with Paige Munro and Dr. Tanja Voegel, but was fully responsible for the 2017 sampling and analysis. I was responsible for cherry fruit harvesting, fruit quality analysis and stem water potential measurements. Several undergraduate work-study students assisted in both years. The analyses of soil chemical and foliar nutrients were performed by the BC Ministry of Environment, Technical Services Lab (Victoria, British Columbia). I wrote my thesis with the guidance of my supervisors, Dr. Melanie Jones and Dr. Louise Nelson, and then it was reviewed by Dr. Thomas Forge who was a member of my supervisory committee. Parts of my results and summary of overall results were prepared as reports to funding agencies via the BC Farm Adaptation Innovator program and published online as: ï· Louise Nelson, Tirhas Gebretsadikan, Paige Munro, Tanja Voegel, Melanie Jones and Julien Picault (2018) Irrigation for Cherry Production Under Climate Change in British Columbia, Farm Adaptation Innovator Program- Research Factsheet for Climate Action Initiative (Available at: https://www.bcagclimateaction.ca/wp/wp-content/media/FI12-2018-factsheet-Postharvest-Deficit-Irrigation.pdf). vi ï· Louise Nelson (2018) Expanding Cherry Production in British Columbia under Climate Change, Final Report for BC Farm Adaptation Innovator Program (Available at: https://www.bcagclimateaction.ca/wp/wp-content/media/FI12-Expanding-Cherry-Production-BC-Climate-Change-2018-report.pdf). vii Table of Contents Abstract .................................................................................................................................... iii Lay Summary ........................................................................................................................... iv Preface ....................................................................................................................................... v Table of Contents .................................................................................................................... vii List of Tables ............................................................................................................................ xi List of Figures ......................................................................................................................... xv List of Abbreviations ............................................................................................................ xvii Acknowledgements ............................................................................................................. xviii Dedication ............................................................................................................................... xx Chapter 1: Introduction ........................................................................................................... 1 1.1 Sweet cherry production and economic importance .............................................. 1 1.2 Management of tree fruit orchards ........................................................................... 1 1.2.1 Mulching - application of organic materials to the orchard floor .................. 2 1.2.2 Regulated deficit irrigation ............................................................................... 5 1.3 Plant water status ...................................................................................................... 7 1.4 Plant responses to water deficit ............................................................................... 9 1.5 Soil health and its indicators .................................................................................... 9 1.5.1 Chemical indicators .........................................................................................11 1.5.2 Biological indicators ........................................................................................13 1.5.2.1 Microbial biomass ......................................................................................13 1.5.2.2 Soil microbial activity ..................................................................................14 1.5.2.3 Arbuscular mycorrhizal fungi colonization ...................................................15 1.5.2.4 Plant-parasitic nematodes ..........................................................................17 1.6 Fruit quality ...............................................................................................................19 1.7 Background of the thesis .........................................................................................21 viii 1.8 Research objectives and hypotheses .....................................................................22 Chapter 2: Materials and Methods .........................................................................................25 2.1 Description of the experimental sites .....................................................................25 2.2 Experimental design and treatments ......................................................................27 2.3 Analysis of water potential, growth, nutrients and fruit quality of sweet cherry trees ...........................................................................................................................30 2.3.1 Stem water potential (Ψstem) ............................................................................30 2.3.2 Tree trunk cross sectional area and leaf area ................................................30 2.3.3 Foliar nutrient content .....................................................................................31 2.3.4 Fruit quality analysis .......................................................................................31 2.4 Analysis of soil and root biochemical properties ..................................................32 2.4.1 Soil and root sampling and preparation .........................................................32 2.4.2 Soil physicochemical property analysis ........................................................33 2.4.3 Soil microbial activity ......................................................................................35 2.4.4 Extraction of nematodes from soils and roots ..............................................35 2.4.5 Root colonization by arbuscular mycorrhizal fungi ......................................37 2.4.6 Quantification of total soil fungi and bacteria................................................38 2.5 Statistical analysis ...................................................................................................40 Chapter 3: Results ..................................................................................................................43 3.1 Soil moisture content and cherry tree water status at three sites ........................43 3.2 The response of soil chemical properties to mulches and postharvest deficit irrigation ....................................................................................................................54 3.3 The response of soil biological properties to mulch treatment and postharvest deficit irrigation ........................................................................................................59 3.4 Responses of sweet cherry tree growth and foliar nutrients to mulch and postharvest deficit irrigation ...................................................................................63 ix 3.5 Impact of mulches and postharvest deficit irrigation on fruit yield and quality...66 3.6 Responses of soil chemical and biological properties at Site 4 ...........................69 Chapter 4: Discussion ............................................................................................................75 4.1 Effect of mulches on soil moisture content and chemical properties ..................75 4.2 Soil biological properties as affected by compost and woodchips ......................76 4.3 Effects of mulches on plant water status, foliar nutrients and fruit quality .........79 4.4 Impacts of postharvest deficit irrigation on soil moisture content and biochemical properties ............................................................................................81 4.5 Effect of postharvest deficit irrigation on plant water stress, growth and fruit yield and quality .......................................................................................................83 4.6 Effects of fumigation, compost and legacy of P-fertigation and mulch on soil chemical and biological properties at Site 4...........................................................85 Chapter 5: General conclusion ..............................................................................................87 Bibliography ............................................................................................................................ 93 Appendices ........................................................................................................................... 118 Appendix A: Responses of average daily maximum and minimum soil moisture contents to organic amendment and irrigation treatments. ................... 118 Appendix B: Regression figures showing relationship between soil moisture content and stem water potential ........................................................................... 126 Appendix C: Effects of mulches and postharvest deficit irrigation on soil chemical properties at each site ............................................................................... 129 Appendix D: Responses of soil biological properties to mulches and postharvest deficit irrigation at each site ..................................................................... 144 Appendix E: Impacts of mulches and postharvest deficit irrigation (PDI) on TCSA, leaf area and cherry foliar nutrients ......................................................... 149 x Appendix F: Effects of mulches and postharvest deficit irrigation on fruit yield and quality at each site .................................................................................... 156 Appendix G: Responses of soil chemical properties to soil amendment at Site 4 in every season. ............................................................................................. 157 Appendix H: Responses of soil biological properties to soil amendment at Site 4 in every season. ............................................................................................. 163 Appendix I. Interaction effects of irrigation and mulch .............................................. 165 xi List of Tables Table 1.1 Fruit quality standards [skin color rating (from 1â light red to 5 â very dark red), firmness, stem pull force, soluble solid concentration (SSC)]. .....................................21 Table 2.1 Detailed description of experimental sites and planting materials. .............................26 Table 2.2 Chemical composition of compost and woodchips used as organic materials. ..........29 Table 3.1 Average monthly mean volumetric soil moisture contents as affected by postharvest deficit irrigation and mulch treatments at Sites 1, 2 and 3. ..........................................45 Table 3.2 The responses of stem water potential (á´ªstem) (MPa) of sweet cherry trees to mulch treatments at three sites and for both mulch and irrigation at each site .......................53 Table 3.3 Impact of mulch on different forms of carbon including C (total carbon), POXC, OrgC (organic C), InoC (inorganic C) and on OM (organic matter), C:N ratio and Mn. .........55 Table 3.4 Effects of postharvest deficit irrigation on different forms of carbon [C (total carbon), POXC, OrgC (organic C), InoC] and on OM, C:N and Mn at Sites 2 and 3. ................56 Table 3.5 Responses of soil nutrients to mulch treatments over three sites and two years. ......57 Table 3.6 Responses of soil nutrients to postharvest deficit irrigation over two sites and two years. .........................................................................................................................58 Table 3.7 Impact of mulch and postharvest deficit irrigation on extractable Al and Na contents, cation exchange capacity, pH and electrical conductivity in soil. .................................59 Table 3.8 Effects of mulch treatment on soil biological properties at three sites in two years. ...61 Table 3.9 Effects of postharvest deficit irrigation on soil biological properties at two sites over 2016 and 2017............................................................................................................62 Table 3.10 The effect of mulch treatment on tree trunk cross-sectional area and leaf area across three sites over two seasons and postharvest deficit irrigation over two sites .............64 Table 3.11 Effects of mulch on the concentrations of key plant leaf macronutrients over two growing seasons (2016 and 2017) at three sites. ........................................................65 xii Table 3.12 Impact of mulches on the contents of key plant leaf micronutrients over two growing seasons (2016-2017) at three orchard sites. ...............................................................65 Table 3.13 The effects of mulch on fruit yield and quality of sweet cherry at three sites ............67 Table 3.14 The effects of mulch and postharvest deficit irrigation on fruit yield and quality of sweet cherry at Sites 2 and 3. .....................................................................................68 Table 3.15 Impact of fumigation, compost amendment and legacy effects of mulch and P-fertigation on key soil nutrients in 2016 and 2017at Site 4. .........................................71 Table 3.16 Effects of fumigation, compost amendment and legacy effects of mulch and P-fertigation on different forms of carbon, and on OM and C:N at Site 4. .......................72 Table 3.17 Effects of fumigation, compost amendment and history effects of mulch and P-fertigation at Site 4. .....................................................................................................73 Table 3.18 Effects of fumigation, compost and history effects of mulch and P-fertigation on soil biological properties over two seasons at Site 4. ........................................................74 Table C1 Effects of mulches and postharvest deficit irrigation (PDI) on key soil nutrients over two years and in each of 2016 and 2017 at Site 1. ................................................... 129 Table C2 Effects of mulches and postharvest deficit irrigation on different forms of carbon and nitrogen as well as C:N over two seasons and in 2016 and 2017 at Site 1. .............. 130 Table C3 Effects of mulches and postharvest deficit irrigation (PDI) on soil pH, EC, CEC and Al, Ca, Mn and Na over two seasons and in 2016 and 2017 at Site 1. ........................... 131 Table C4 Effects of mulches and postharvest deficit irrigation on key soil nutrients over two seasons and in each of 2016 and 2017 at Site 2. ..................................................... 132 Table C5 Effects of soil mulches and postharvest deficit irrigation on different forms of carbon and nitrogen as well as C:N over two seasons and in 2016 and 2017 at Site 2......... 134 Table C6 Effects of mulches and PDI on soil pH, EC, CEC as well as Al, Ca, Mn and Na over two seasons and in 2016 and 2017 at Site 2. ........................................................... 136 xiii Table C7 Effects of mulches and postharvest deficit irrigation (PDI) on key soil nutrients over two seasons and in each of 2016 and 2017 at Site 3. ............................................... 138 Table C8 Effects of mulches and postharvest deficit irrigation (PDI) on different forms of C and Nas well as C:N over two seasons and in 2016 and 2017 at Site 3........................... 140 Table C9 Effects of mulches and postharvest deficit irrigation (PDI) on soil pH, EC, CEC as well as Al, Ca, Mn and Na over two seasons and in 2016 and 2017 at Site 3. ................. 142 Table D1 Effects of mulching and postharvest deficit irrigation (PDI) on soil biological properties over two seasons and in 2016 and 2017 at Site 1. .................................................... 144 Table D2 Effects of mulches and postharvest deficit irrigation (PDI) on soil biological properties over two seasons and in 2016 and 2017 at Site 2. .................................................... 145 Table D3. Effects of mulches and postharvest deficit irrigation (PDI) on soil biological properties over two seasons and in 2016 and 2017 at Site 3. .................................................... 147 Table E1 Effect of mulches and PDI on the tree trunk cross-sectional area (TCSA) and leaf area (LA) at three sites over two seasons together and in each season. .......................... 149 Table E2 Effects of mulches and postharvest deficit irrigation on the contents of key plant leaf macro-nutrients over two seasons and in 2016 and 2017 at Site 1. .......................... 150 Table E3 Effects of mulches and postharvest deficit irrigation (PDI) on the contents of key plant leaf micro-nutrients over two seasons and in 2016 and 2017 at Site 1. ..................... 151 Table E4 Effects of mulches and postharvest deficit irrigation on the contents of key plant leaf macro-nutrients over two seasons and in 2016 and 2017 at Site 2. .......................... 152 Table E5 Effects of mulches and postharvest deficit irrigation on the contents of key plant leaf micro-nutrients over two seasons and in 2016 and 2017 at Site 2. ........................... 153 Table E6 Effects of mulches and postharvest deficit irrigation on the contents of key plant leaf macro-nutrients over two seasons and in 2016 and 2017 at Site 3. .......................... 154 Table E7 Effects of mulches and postharvest deficit irrigation on the contents of key plant leaf micro-nutrients over two seasons and in 2016 and 2017 at Site 3. ........................... 155 xiv Table G1 Effects of fumigation, organic amendment and legacy effects mulch and P-fertigation on key soil nutrients in 2016 and 2017 season at Site 4. .......................................... 157 Table G2 Effects of fumigation, organic amendment and legacy effects mulch and P-fertigation on different forms of C and N as well as C:N in 2016 and 2017 season at Site 4. ..... 159 Table G3 Effects of fumigation, organic amendment and legacy effects mulch and P-fertigation on soil pH, EC, CEC as well as Al, Ca, Mn and Na in 2016 and 2017 at Site 4. ........ 161 Table I1 An interaction effect of postharvest deficit irrigation and mulches on total soil nematodes at Site 1, stem water potential at Site 2 and Al and TCSA ...................... 165 Table I2 Interaction effect of fumigation and history on Total N, C, organic matter (OM) and organic C at Site 4. ................................................................................................... 165 Table I3 An interaction effect of fumigation and history on Ca, Al, CEC and EC at Site 4. ....... 165 Table I4 An interaction effect of fumigation and compost on root P. penetrans, Mg and Mn at Site 4. ....................................................................................................................... 166 Table I5 An interaction effect of fumigation, compost and history on total fungal abundance at Site 4. ....................................................................................................................... 166 xv List of Figures Figure 2.1 Images of the compost (a) and woodchips (b) used as soil organic amendment at Site1 and 2 sweet cherry orchards. ..........................................................................29 Figure 3.1 Average daily soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2016 growing season. .............46 Figure 3.2 Average daily soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2017 growing season ..............47 Figure 3.3 Average daily soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2016 at Sites 2 and 3. .......................48 Figure 3.4 Average daily soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2017. ................................................49 Figure 3.5 Effect of mulches on monthly averaged minimum volumetric soil moisture content across three sites over two seasons. .......................................................................50 Figure 3.6 The patterns of stem water potential (á´ªstem) (MPa) of sweet cherry trees at Sites 2 and 3 in 2016 in response to irrigation treatments....................................................51 Figure 3.7 The patterns of stem water potential (á´ªstem) (MPa) of sweet cherry trees at three sites in 2017 in response to irrigation treatments. ............................................................52 Figure A1 Average daily maximum soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2016 .................... 118 Figure A2 Average daily maximum soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2017 .................... 119 Figure A3 Average daily minimum soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2016. ................... 120 Figure A4 Average daily minimum soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2017. ................... 121 xvi Figure A5 Average daily maximum (max) soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2016 at Sites 2 and 3. .. 122 Figure A6 Average daily maximum soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2017. ........................... 123 Figure A7 Average daily minimum soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2016 at Sites 2 and 3. .. 124 Figure A8 Average daily minimum soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2017. ........................... 125 Figure B1 Relationship of daily averaged volumetric soil water contents and stem water potential at Site 1. .................................................................................................. 126 Figure B2 Relationship of daily averaged volumetric soil water contents and stem water potential at Site 2. .................................................................................................. 127 Figure B3 Relationship of daily averaged volumetric soil water contents and stem water potential at Site 3. .................................................................................................. 128 xvii List of Abbreviations Ψ water potential Ψstem stem water potential AMF arbuscular mycorrhizal fungi ANOVA analysis of variance CEC cation exchange capacity DNA deoxyribonucleic acid DI deficit irrigation EC electrical conductivity FDA fluorescein diacetate FI full irrigation GWC gravimetric or volumetric water content LA leaf area OC organic carbon OA organic amendment qPCR quantitative polymerase chain reaction PDI postharvest deficit irrigation POXC permanganate oxidizable carbon RDI Regulated deficit irrigation SSC soluble solid concentrations SuRDC Summerland Research and Development Center SOM soil organic matter TA titratable acidity TCSA Trunk cross sectional area TN total nitrogen TOC total organic carbon xviii Acknowledgements I am highly grateful to all UBC Okanagan faculty, staff and graduate students who have helped me throughout my study. I am particularly grateful to my supervisors Dr. Melanie Jones and Dr. Louise Nelson for giving me the opportunity to work in their labs under their supervision. Their continuous generosity, guidance and unreserved support throughout the study have allowed me to succeed. I am very lucky to get the chance to study under their supervision. I would also like to thank Dr. Thomas Forge who served as my advisory committee member for his kindness, unreserved technical advice and research inputs during my study. I would like to extend my special gratitude to Paige Munro for her openness and being there whenever I needed her all the time and making my early transition fruitful. I would also like to thank all past and present members of Dr. Melanie Jones and Dr. Louise Nelson lab groups. My special thanks go out to Dr. Tanja Voegel, Tristan Watson and Joshua Smith for their guidance and support through my study, and to Jill Forsyth, Reece Blake, Dave Emde, Sarah Sweazey, Janine Siopongco and Emilie Tremblay for their generous assistance and support during sample collection and analysis. I thank Gayle Krahn and David Geen (Coral Beach Farms Ltd) and Niel Dendy (Dendy Orchards) for allowing us to conduct this research with their generous and unreserved support, and Denise Neilsen for allowing us to use her lab facilities, Shawn Kuchta and Paul Randall (SuRDC) for their technical and methodological support in field and lab analysis. Without their kind cooperation, I wouldn't have succeeded. Additionally, I would like to express my sincere appreciation to Dr. Tessema Astatkie (Statistics Professor at Dalhousie University) for guidance and support in statistical analysis. I would like to thank my special friends Antreas Pogiatzis for a support in cherry fruit harvesting, and Vasilis Kokkoris, Bruno Carturan, Melissa Larrabee, Eric Vukicevich and Justin Meeds for their guidance in statistical analysis. xix My sincere appreciation goes to Agriculture and Agri-Food Canada (AAFC) and the BC Ministry of Agriculture for funding support through the BC Agriculture & Food Climate Action Initiative under Growing Forward 2 (a federal-provincial-territorial initiative), which was delivered by the Investment Agriculture Foundation of BC. I would also like to thank UBC Okanagan for the financial assistance through the awards of UBC entrance scholarship and University Graduate Fellowship during my study. Lastly, I would like to acknowledge my families for being the source of my encouragement and continuous support. Special thanks to my husband Ayelign Mengesha Adal for being with me throughout every up and down during my academic and personal life. xx Dedication To my husband, family and supervisors 1 Chapter 1: Introduction 1.1 Sweet cherry production and economic importance Sweet cherry (Prunus avium L.) is one of the most popular fruits worldwide. According to a recent FAO (2017) report, world cherry production was estimated at over 2000 metric tons, 43% of which was produced by the top three cherry producing countries, i.e., Turkey, USA and Iran. Despite the dominant production in these countries, sweet cherry is also an economically important product in Canada. The market value for commercial production of this fruit has been increasing, and sweet cherry has become one of the top ten fruits grown in Canada (Agriculture and Agri-Food Canada, 2013). In particular, British Columbia (BC) is the top producer of sweet cherries, accounting for 89% of the total production of the country (Neilsen et al., 2014; BC Ministry of Agriculture, 2014). In 2014, about 14,000 metric tonnes of sweet cherries were produced out of 126,000 tonnes of total tree fruits produced in BC. This province had nearly 6,000 hectares covered with fruit tree orchards. Of this area, 22% comprised orchards of sweet cherries (BC Ministry of Agriculture, 2014). The major production areas in this province include the Okanagan, Similkameen and Kootenay Valleys, with the majority located in the Okanagan Valley. Export of BC cherries to different countries increased by about 56 % in 2015 from the previous year (2014) to 13,600 metric tonnes, with a value of $91.7 million (70% increase) (BC Ministry of Agriculture, 2016). The nationwide marketed sweet cherries production declined by 3.6 % in 2016 relative to the previous year (Statistic Canada, 2017); however, production from BC increased by 48 % compared with cherries produced in 2016 (Statistic Canada, 2018). 1.2 Management of tree fruit orchards Fruit trees are high input crops requiring management of orchard floor and trees, including application of pesticides/herbicides, fertilizer and irrigation; pruning; and fruit 2 harvesting. These management practices are very important to tree growth and health, fruit yield and quality, and soil health. In particular, proper orchard floor management is useful to stabilize or improve the soil physico-chemical properties, to maintain beneficial microbes, and to minimize maintenance and production inputs like irrigation water and fertilizers (Abbas and Fares, 2009; Rowley, 2011). In addition, irrigation is one of the most important orchard management practices for fruit production in the Okanagan where average growing season precipitation is low. The quantity and the application rate of irrigation water required by the trees are determined by different factors including orchard floor management, weather and plant phenology. Best irrigation management practices are adopted to optimize use of irrigation water in different fruit trees. By developing appropriate irrigation scheduling, including the right amount and right timing of irrigation, application of excess water can be avoided (Abbas and Fares, 2009; Xiloyannis et al., 2012). 1.2.1 Mulching - application of organic materials to the orchard floor Nowadays, several orchard floor management practices are available to improve soil function and alter the soil biological community. Mulches refer to organic or inorganic materials that are spread over the surface of the soil for various purposes. Mulch is used in agricultural fields to keep the soil surface free from vegetation, particularly weeds; conserve soil moisture; reduce variability of soil temperature; increase soil organic matter through decomposition; release nutrients to the soil; and improve the soil environment by enhancing microbial activity (Stefanelli et al., 2009; Rowley, 2011). Mulching with inorganic materials (thin sheets of plastic) has been widely used in different agricultural fields to control soil microclimates. However, the benefits of inorganic mulches are limited to avoidance of weed seed contamination in the mulching materials, preventing insect pests and controlling soil moisture and temperature (Kasirajan and Ngouajio 2012). On the other hand, organic materials, such as compost, 3 woodchips, straw and other plant materials, which are spread or laid over the surface of the soil as a covering, can play a significant role in agricultural productivity and soil health. They can slowly decompose, releasing organic matter and mineral nutrients to the soil; these nutrients and organic matter, in turn, influence soil microbes and plant productivity. For example, shredded paper, sawdust and biosolids, applied as mulches to apple orchards, had a positive impact on soil bioactivity, nematode communities and plant productivity (trunk diameter, shoot growth and yield) (Forge et al. 2003; Neilsen et al. 2003a; Forge et al. 2008; Sas-Paszt et al. 2014). Water retention and a decrease in evaporation in potato plants by straw mulch have also been documented (Kar and Kumar 2007). A study conducted on mandarin trees indicated that cattail or rice straw mulches successfully controlled weeds and increased fruit yield (Abouziena et al. 2008). Application of organic soil amendments, including compost and wood/bark chips, to the surface of the orchard floor (i.e. mulch) is commonly used to increase soil organic matter in fruit tree orchards. Additionally, it can increase fruit productivity while reducing disease-causing pathogens (Forge et al. 2003; Neilsen et al. 2003a; McSorley 2011; Grabau and Chen 2014; Mehta et al. 2014; Sas-Paszt et al. 2014; Forge et al. 2016). Enhancement of soil organic matter via mulching with any organic material can have multiple benefits. Soil organic matter (SOM) is the fraction of the soil consisting of plant and animal residues at various stages of decomposition, as well as soil microbes (Murphy 2014). Soil organic matter is the primary source of energy and nutrients for soil microbes, and also increases water holding capacity, aggregate stability and soil aeration (Scotti et al. 2013; Scotti et al. 2015). It improves soil chemical properties by increasing the ability of soil to resist pH change and to accelerate break-down of soil minerals (Scotti et al. 2013). It also enhances soil biological properties through increasing soil microbial biodiversity and activity, which can help in the suppression of diseases and pests, and enhance pore space through the action of soil 4 microbes (Abawi and Widmer 2000; Larkin 2015). The loss of SOM in the soil can limit plant growth due to problems of lower soil fertility, erosion, compaction, reduced water availability, soil borne disease and other soil health-related problems (Abawi and Widmer 2000; Scotti et al. 2015). Therefore, increases in organic matter via strategies such as addition of compost or organic mulches, retention of crop residues, crop rotations (e.g., legume or grasses) and cover crops, can improve crop production and enhance soil suppressiveness for pathogens (Briar et al. 2007; Scharenbroch and Watson 2014). Composts are organic materials that have been partially biologically decomposed by microbial activity (Dees and Ghiorse 2001). As long as they have passed through a thermophilic phase (to 80oC) compost amendments have emerged in recent years as the safest and most accessible amendments to improve soil health in fruit tree orchards. Compost can be applied through two methods: incorporation and mulching. With incorporation, compost is mixed into the top few centimeters, providing greater accessibility for soil microbes and plant roots (Cogger et al. 2008). In tree fruit production systems, opportunities to incorporate compost into soil are limited to when an orchard is being prepared for replanting or being planted for the first time. Although compost incorporation has a more rapid effect on carbon, nitrogen and bulk density than mulching, application of compost as a mulch (on the surface of the soil) fulfills the functions of mulch as described above, improves soil health, and provides plant nutrients (Cogger et al. 2008). Depending on the type of materials used in the compost, surface-applied compost can enrich soil with organic matter that can improve soil biological properties (Haynes 2008). Such compost-derived organic matter has shown a positive effect on soil microbial communities that are associated with diverse fruit trees, including apples (Forge et al. 2016).It can also reduce nematode populations and improve plant growth in apple orchards, and can eventually alleviate replant problems (Forge et al., 2016; Thoden et al., 2011). 5 Unlike plant growth and development, few reports are available on the interaction of tree fruit quality and organic mulches. For instance, apple orchards amended with different composts produced fruits with higher quality attributes, like fruit firmness, compared with other management systems (Peck et al. 2006). A recent report from Australia showed that compost amendment for two seasons improved soluble solid concentration (SSC), and decreased fruit size, firmness and stem pull force of sweet cherry, thus maintaining its export standard (Tan et al. 2018). On the other hand, use of bark mulch improved fruit quality of sweet cherry during the first few growing seasons (Neilsen et al. 2006), but not after seven growing seasons (Neilsen et al. 2014). By contrast, soluble solid concentration of mandarin oranges was not affected by use of organic mulches like rice straw and cattail mulch (Abouziena et al. 2008). Overall, despite the fact that mulching with organic materials is widely used worldwide, it has not been a common practice in fruit orchards, particularly in sweet cherry orchards, in the Okanagan Valley. Additionally, there are no reports on the effect of organic mulches on cherry tree growth, water status, productivity and fruit yield and quality on newly established orchards that have never been fumigated in this region. 1.2.2 Regulated deficit irrigation Because of climate change, population growth, and the expansion of various industrial sectors (e.g., agriculture, energy and mining), the demand for water has been increasing over time (UNESCO, 2012). Sustainable and effective water supply mechanisms, such as appropriate irrigation practices, are indispensible to maintain the productivity of fruit trees (Marsal et al. 2010). However, irrigation water has been over-exploited in most agricultural regions, which often leads to a shortage of freshwater, particularly in arid, semiarid and mountainous areas (Chai et al. 2014). Over the last few decades, many orchards have adopted deficit irrigation strategies to maintain optimum productivity while minimizing water usage. 6 Regulated deficit irrigation (RDI) refers to a supply of less irrigation water to crop plants than their total water requirements during certain growth stages. Specifically, RDI is used during stages where fruit trees are least sensitive to water stress; if reductions are not too large, yield and fruit quality are not affected (Goldhamer and Beede 2004; Behboudian et al. 2011; Chai et al. 2014). It is a useful practice to save irrigation water, improve plant water stress tolerance, and increase plant water use efficiency (Ruiz-Sanchez et al. 2010; Behboudian et al. 2011; Chai et al. 2016). For example, many fruit trees grow well under RDI, including high density orchards of apple, pear and peach trees (Costa et al., 2007; Lopez et al., 2008). Use of RDI during pre-harvest (at stage II, pit hardening) and/or during the post-harvest growing season of peach trees in Spain significantly reduced vegetative growth, without influencing fruit production until the fourth year, when fruit set decreased slightly with combined pre- and postharvest RDI (Girona et al. 2005). In addition, RDI applied in stage I (reproductive cell division) and II (pit hardening) of fruit growth had no impact on fruit yield of nectarines in Israel (Naor et al., 1999). In Spain, regulated deficit irrigation applied for three years in stage III (final fruit growth) also improved mandarin fruit quality, but reduced leaf mineral nutrition, including N and Mg contents, depending on the rootstock (Romero et al. 2006). Using postharvest RDI (50 % reduction), sweet cherry fruit yield was maintained at a similar level to fully irrigated trees in Spain while saving nearly 50 % of irrigated water (Marsal et al. 2010; Behboudian et al. 2011). However, several reports suggest that RDI application needs extra caution as severe water stress during the post-harvest period could cause yield and fruit disorders in the following year (Girona et al. 2005; Naor et al. 2005; Naor 2006; Lopez et al. 2007; Behboudian et al. 2011). For example, postharvest water stress led to decreased crop yield of nectarine in the subsequent year (Naor et al. 2005). It also has been reported that RDI significantly reduced the yield and fruit size of peach, but not of plum (Razouk et al., 2013; Samperio et al., 2015). No reports are available on 7 RDI implementation on sweet cherry orchards in the Okanagan Valley. The closest thing was a study examining the effect of high or low irrigation frequency with the same amount of applied water (Neilsen et al. 2006); in that case, the yield of Okanagan sweet cherry trees was not affected by irrigation frequency. Most fruit quality parameters are affected by water deficits, depending on the type of fruit trees. An increased soluble solid concentration (SSC), indicating higher sugar content that leads to high preference by consumers, and a decreased titratable acidity (TA) were detected from peach and plum fruits grown under RDI (around 50% reduction) in Spain (Gelly et al., 2004). Regulated deficit irrigation improved other quality parameters of peach fruit, such as sweetness, juiciness, and the intensity of flavor, but not firmness and crispness (Vallverdu et al., 2012). In Spain, application of postharvest RDI did not affect the yield and immediate fruit quality of sweet cherry; however, after cold storage, some quality attributes including firmness and SSC were slightly reduced (Marsal et al. 2010). A study with different levels of irrigation application in sweet cherry planted in Turkey showed that none of irrigation water levels affected TA, SSC, pH and fruit weight of sweet cherry (Correia et al. 2017). In the Okanagan, higher SSC and color, as well as lower TA, firmness, and stem pull force were reported from orchards where the same amount of water was applied, but at lower frequencies (Neilsen et al. 2006; Neilsen et al. 2014). No studies have examined the effect of RDI on the fruit quality of sweet cherries in the Okanagan Valley. 1.3 Plant water status Plant water status at cell, organ (i.e., leaf or flower) or whole-plant levels is influenced by environmental and agronomic practices, thereby influencing plant growth and development, and plant response to environmental cues (Jones 2007). Plant water status is a crucial parameter in 8 irrigation studies, and provides a platform to determine water stress in the plant (Naor 2006; Behboudian et al. 2011). It can be determined by water potential (Ψ), which is the tendency of water to move from one area to another in plants, soil and air, relative to pure water at atmospheric pressure. Water potential has been reported as a sensitive physiological indicator of the water status of the plant (Remorini and Massai 2003; Jones 2007). Measuring the Ψ at various times of the day and/or during certain conditions is the most common approach for monitoring plant water status. Various types of Ψ can be measured on plants, including leaf water potential (Ψleaf) at midday (Ψmd) or predawn (Ψpredawn), and stem water potential (Ψstem). Leaf water potential is measured on a single mature leaf that remains exposed to ambient conditions at least an hour prior to measurement; mid-day measurements are made between 1000 and 1400. Ψpredawn is measured on uncovered leaves at the end of the night (between two hours prior to, and at, dawn). Stem water potential is determined on a non-transpiring leaf that had been bagged with both a plastic sheet and aluminum foil for at least an hour before measurement. These Ψs are typically measured using a pressure chamber (Naor 2006; Baeza et al. 2007). Comparatively, Ψstem is more sensitive and accurate than Ψleaf in indicating the amount of stress experienced in the actual plant trunk at the time of measurement (Naor, 2006). By contrast, Ψleaf shows the amount of stress in only that particular leaf at that moment (McCutchan and Shackel 2019). According to Behboudian et al. (2011), Ψstem is currently considered as the best approach to measure water status of irrigated fruit trees. Stem water potential has been successfully applied as a water deficit and irrigation scheduling indicator on fruit trees, such as apple, peach and prune, as well as grapevines (McCutchan and Shackel 1992; Choné et al. 2006; Naor 2006; Behboudian et al. 2011). A defined safe Ψstem value for plant growth and development has not been established for most fruit trees. Nevertheless, potted apple seedlings exhibited reduced stem growth when their Ψstem 9 was less than around -1.5 MPa (De Swaef et al. 2009). By contrast, stomatal closure has been reported at a relatively low Ψstem of -2.5 MPa in mature apple trees in response to water stress in the field (Naor et al. 2008; De Swaef et al. 2009). 1.4 Plant responses to water deficit Water is one of the major environmental factors that limits plant growth and productivity in the world (Ings et al. 2013; Osakabe et al. 2014). Water deficit scan be caused by either a restriction in water supply or imbalance among water absorption, translocation and loss. Plant water stress impacts several physiological and developmental processes that affect fruit production, including flowering, and fruit development (Bray 1997; Bray 2001; Chavarria and dos Santos 2012; Bhattacharjee and Saha 2014; Osakabe et al. 2014). In particular, it induces a decrease in leaf water potential and in stomatal opening that leads to the reduced availability of CO2 and the down-regulation of photosynthesis-related genes (Osakabe et al. 2014). The plant responses to water stress may involve stress avoidance and/ or tolerance. Water stress avoidance involves distinct responses of the plants, such as increased stomatal and cuticular resistance, decreased leaf area, changes in leaf orientation and increased root density and depth (Chaves et al. 2002; Basu et al. 2016). On the other hand, plant water stress tolerance includes maintaining adequate cell turgor without cellular metabolism disruption through osmotic adjustments (e.g. inorganic ions, carbohydrates and organic acids) and changes in cellular/ tissue elasticity (Chavarria and dos Santos 2012; Bhattacharjee and Saha 2014; Feng et al. 2016). 1.5 Soil health and its indicators Over the previous few decades the concept of soil quality was used interchangeably with that of soil health (Bruggen and Semenov 2000). The Soil Science Society of America Ad Hoc 10 Committee on Soil Quality (S- 581) set an acceptable definition of soil quality as âthe ability of a specific kind of soil to function within its capacity and within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human habitation and healthâ (Karlen et al., 1997). However, the term soil health is preferred because it specifically describes the soil as a living entity within a dynamic system. Soil health refers more broadly to the functionality of a soil to maintain environmental quality, plant and animal health, and biological productivity, while the term soil quality is associated with the fitness of the soil for a specific purpose (Bruggen and Semenov 2000; Doran and Zeiss 2000; Doran 2002; Cardoso et al. 2013). In particular, the concept of soil health refers to the biological, chemical and physical features necessary for long-term and sustainable agricultural productivity with minimal environmental impacts (Bruggen and Semenov 2000; Arshad and Martin 2002; Arias et al. 2005; Cardoso et al. 2013; Larkin 2015). Therefore, in this thesis, I will use the concept of âsoil healthâ when referring to the effects of organic amendments and deficit irrigation on soil. Although soil ecosystem functioning is difficult to measure directly, relevant soil physical, chemical and biological indicators have been widely used (Larney and Angers 2012). Physical indicators include bulk density, texture, porosity, water holding capacity, penetration resistance and aggregate stability. It has been reported that soil porosity and aggregate stability were improved by addition of organic amendment (Leroy et al. 2008; Karami et al. 2012; Larney and Angers 2012). Soil chemical parameters, such as soil pH, cation exchange capacity (CEC), electrical conductivity (EC), total soil organic matter (SOM), extractable nitrogen, phosphorus and potassium as well as minor element contents, can all serve as indicators of effective soil functioning (Cardoso et al. 2013). Additionally, biological indicators of soil health include microbial biomass, soil enzyme activities, total bacterial and fungal biomass, soil respiration, and earthworm, free living nematode, arthropod and protozoa populations (Kirk et al. 2004; 11 Arias et al. 2005; Caldwell 2005; Allen et al. 2011; Murphy 2014; Larkin 2015). In this thesis, I measured both chemical and biological indicators of soil health. These indicators are elaborated on below. 1.5.1 Chemical indicators Chemical properties, such as soil organic matter (SOM), pH, cation exchange capacity (CEC) and electrical conductivity (EC) are important indicators of soil health (Cardoso et al. 2013). As described in section 1.2.1, soil organic matter is one of the key chemical indicators of soil health, and has a strong association with soil biological properties. Soil organic matter is measured through the most commonly used methods total organic carbon and loss of ignition. Total organic carbon (TOC) has mainly been considered as a dominant elemental constituent of SOM and used to quantify the stock and changes in organic matter after different agricultural practices (Weil et al. 2003). Total organic carbon can further be fractionated into stable and active (labile) carbon based on decomposition rate and size of the molecules. The stable carbon fraction is the major component, accounting for 90% of the TOC and is protected from decomposition, resulting in a very slow turnover rate (Wang et al. 2017; Sainepo et al. 2018). By contrast, active carbon is a relatively small fraction of TOC that has a short life in soils and responds quickly to changes in soil management practices (Weil et al. 2003; Wang et al. 2017; Romero et al. 2018). Active organic carbon in the soil can be assessed using different methods, such as permanganate-oxidizable C (POXC) and mineralizable C (Hurisso et al. 2016). POXC refers to organic matter oxidized in potassium permanganate solution and determines the active carbon easily available to soil microbes (Culman et al. 2012; Hurisso et al. 2016). The POXC method can be useful to determine soil management-derived changes in SOM (Culman et al. 2012). On the other hand, mineralizable C, which is determined by measuring the flush of CO2 from rewetted air dried soil during a short-term incubation, indicates 12 short-term nutrient availability in soils. Because of its sensitivity, ease of use and low cost, POXC is preferred over mineralizable C (Hurisso et al. 2016). Soil organic matter in agricultural soil enhances the availability of major nutrient cations, such as Ca, magnesium (Mg) and potassium (K), and immobilizes potentially toxic cation (e.g. aluminum (Al)) (Dalal et al. 2003; Allen et al. 2011). Soil organic matter contains all of the plant macronutrients (N, Ca, K, Mg, phosphorus (P), and sulphur (S)), as well as many micronutrients (manganese (Mn), boron (B), chlorine (Cl), copper (Cu), iron (Fe), molybdenum (Mo) and zinc (Zn)). It can serve as the main source of N, P, and S found in soils. For example, corn plants cultivated in rotation with soybean took up most of their N from soil organic pools rather than from supplied inorganic N fertilizer during that particular growth season (Omay et al. 1998). Depending on the type of materials used, addition of SOM via organic soil amendment influences the content of most nutrients in soil. For instance, organic amendment using materials like manures provides high nutrient content to soil (Zhao et al. 2009), while other materials including straw or woodchips can negatively affect the soluble concentrations of some macronutrients (e.g., N and P) through immobilization by soil microbes (Diacono and Montemurro 2010). Soil pH is one of the most important chemical properties contributing to soil health because it strongly affects soil nutrient availability and cycling, soil biological activity and crop performance (Cardoso et al. 2013). Most soils used for growing plants have a pH close to neutral (pH = 5.5 to 7.5), but the original pH can be altered by acidifying processes, such as the application of ammonium-based fertilizers, and the leaching of excess nitrate and accompanying cations (e.g. calcium (Ca) as a result of excess irrigation. However, SOM increases soil buffering capacity, thereby resisting soil pH changes (Dalal et al. 2003). 13 Cation exchange capacity (CEC) is positively related to the percent of the clay and organic matter in soils, and ability of the soil to store cations. Soils with high CEC can hold more cation nutrients and water, while soils with low CEC become more acidic (low pH and high Al concentration) and have low water holding capacity (Baquy et al. 2018). Electrical conductivity (EC) is also a measure of the number of ions or dissolved salts in the soil solution (Arias et al. 2005). High EC can be detrimental to crop growth because of specific ion toxicity (e.g., Na) and presence of high osmotic pressure around the root preventing efficient water absorption; and that together can lead to soil and water salinity and limitations to crop growth (Dalal et al. 2003). 1.5.2 Biological indicators Soil biological properties, such as microbial abundance, soil enzyme activity and microbial diversity are the most sensitive indicators of changes in the soil processes (Cardoso et al. 2013). Changes in these variables are linked to alteration of biologically-available forms of SOM (Haynes 2008). Microbial biomass is the living component of soil organic matter, and comprises the most labile carbon (C) pool in soils (Haynes 2008). The existence of high microbial biomass in soils can facilitate suitable growth conditions for the plants by increasing the availability of soil nutrients and possibly creating antagonistic conditions against plant disease-causing pathogens (Hoitink and Boehm 1999). Soil microbial biomass can often be enhanced by supplying C-rich organic matters as soil organic amendments (Wiseman et al. 2012). 1.5.2.1 Microbial biomass Different soil microbial biomass (particularly bacterial and fungal biomass) detection methods have been reported, including culture-based techniques, phospholipid fatty acid (PLFA) analyses and quantitative PCR (qPCR) (Blagodatskaya and Kuzyakov 2013). Culture-14 based techniques use growing media to investigate fungal and bacterial populations from the soil samples (Kirk et al. 2004), but over 99% of the microorganisms in any environment observed through a microscope are not culturable using lab media (Mehta et al. 2014). PLFAs are found only in living cellular membranes. Specific fatty acids can serve as biomarkers to differentiate and/ or quantify microbial biomass and to characterize microbial community structure (Blagodatskaya and Kuzyakov 2013). However, PLFA analysis offers limited insight into changes in specific microbial populations (Joergensen and Wichern 2008). It may also underestimate fungal biomass due to the limited number of signature fatty acids for fungi. By contrast, qPCR is simple, reproducible, sensitive and quantitative, and is used widely to quantify microorganisms from any environment (Mehta et al. 2014). It targets mostly bacterial (16S) and fungal (18S)-specific gene regions, and produces the gene copy numbers in relation to the size of the targeted microbial group (Blagodatskaya and Kuzyakov 2013). This approach also has limitations, such as targeting both active and inactive cells, and multiple cells (e.g., in fungi), which causes inappropriate estimation in a given sample (Strickland and Rousk 2010). 1.5.2.2 Soil microbial activity Soil microbial activity reflects how soil functions in terms of soil nutrient (e.g., C and nitrogen (N)) cycles and organic matter decomposition (Haynes 2008). The activity of soil microbes depends on the organic matter in soils, soil type, and the type and application rate of soil organic amendment (Albiach et al. 2000). Microbial activity in soils is sensitive to soil disturbance and to changes in soil management (García-Ruiz et al. 2009). There are different enzymatic assays employed to determine soil microbial activities depending on the purpose (Caldwell 2005). Most of the enzymatic activity assays, such as β-glucosidase, phosphomonoesterases, urease and dehydrogenase, are involved in targeting specific enzyme activities. However, fluorescein diacetate (FDA) hydrolysis is used as a general indicator of soil 15 hydrolytic activity by measuring the combined activity of proteases, lipases and esterases (Taylor et al. 2002; Green et al. 2006). The colourless FDA is hydrolyzed by both free and membrane-bound enzymes of both fungi and bacteria (e.g., esterases, proteases and lyases) into a coloured end product called fluorescein. Given that, the FDA assay provides a good estimate of microbial activities resulting from the whole microbial community involved in FDA hydrolysis. 1.5.2.3 Arbuscular mycorrhizal fungi colonization Arbuscular mycorrhizal fungi (AMF) are beneficial soil microbes that establish a symbiosis with roots of the vast majority of plants (Khade and Rodrigues 2009; Wu et al. 2011; Wu et al. 2013). The main fungal structures involved in the symbiosis include hyphae, arbuscules and vesicles (Klironomos and Hart 2002; Souza 2015). Briefly, hyphae are a strand-like series of cells that originate from germinating spores and grow through the soil to the plant root. Once inside the root cortical zone, hyphae can form arbuscules, which are branched or coiled structures within root cortex cells. Arbuscules are considered to be the most useful site of exchanging substances between AMF and the host plant roots (Luginbuehl and Oldroyd 2017). Hyphae can also differentiate into vesicles within or between root cortex cells. Vesicles contain high levels of lipids and glycogen, thereby playing an important role in energy storage. Finally, hyphae are able to produce new asexual spherical spores in the soil to maintain the life cycle of the AMF. After colonization of the root, AMF mycelia can proliferate in the soil, taking up water and minerals (particularly phosphorus), and transferring some of these from soil to the host plant. In turn, the plant provides carbohydrates to the fungi (Wu and Xia, 2006; Wu et al., 2011, 2013). It has been reported that AMF colonization improves the water relations of many plants (Wu and Xia, 2006; Wu et al., 2013); however, the mechanisms by which AMF improve water stress 16 tolerance and water flow through host plants remain unknown. They may include water absorption by external hyphae, stomatal regulation by hormonal signals, and increased osmotic adjustment in mycorrhizal plants (Wu et al. 2013). Several studies have shown that AMF have essential roles in many woody perennial fruit crops such as grapevines, citrus, apple, peach, and cherry (Pinochet et al. 1996; Calvet et al. 2004; Meyer et al. 2015; Berdeni et al. 2018). In relation to water stress, AMF colonization significantly stimulated plant growth and biomass regardless of water deficit in citrus seedlings (Wu and Xia, 2006). The response of AMF to organic and inorganic soil amendments can vary. AMF abundance and diversity are negatively affected by inorganic fertilizer application due to excess supply of N and P to the soil and changes in soil pH (Liu et al. 2012). By contrast, root colonization by AMF was enhanced by addition of compost to soybean and other plants (Cavagnaro 2015; Saia et al. 2015; Yang et al. 2018). However, in maize and tomato, addition of compost had a negative or neutral effect on AMF biomass and colonization (Cavagnaro 2015; Cozzolino et al. 2016). The level of AMF colonization can be inversely correlated to plant available P in compost (Cavagnaro 2015). Microscopy and qPCR quantifications can both contribute to the identification and/or quantification of AMF colonization in roots (Alkan et al. 2004; Vierheilig et al. 2005; Sun and Tang 2012). Microscopic methods have been used in AMF studies for a long time, having the advantage of indicating the length of root colonized and the types of AMF structures present. They can be aided (but not replaced) by qPCR, which is a sensitive method for providing a proxy of AMF abundance by quantifying the specific target gene regardless of the structures (Alkan et al. 2004). 17 1.5.2.4 Plant-parasitic nematodes Nematodes are the most abundant multicellular organisms on earth; they can be free living or plant parasites. Most free-living nematodes are considered beneficial to plants, playing a role in decomposing organic matter, and preying on other nematodes, soil-borne insects, and pathogenic fungi and bacteria. Plant parasitic nematodes, on the other hand, are economically important pests feeding in or on plant roots (Briar et al. 2007; Pokharel et al. 2015). Of these plant parasitic nematodes, root-lesion nematodes (Pratylenchus spp.) are migratory endoparasites and common pathogens in most agricultural and horticultural crops, such as temperate fruit trees across the globe (Forge et al. 2008; Pokharel et al. 2015; Forge et al. 2016). Pratylenchus penetrans attacks a broad range of plants. Although this species can invade most types of roots, they prefer the dense root-hair zone or young feeder roots (Zunke 1990; Pinochet et al. 1996; Smiley 2015). Female and juveniles select potential invasion sites of the roots by rubbing the epidermal cells with their lips and stylet. Then, the stylet is thrust into the root cells, creating holes in the cell wall through which the nematodeâs head penetrates. Finally, the nematode injects saliva prior to ingesting the cell contents. At the feeding sites, lesions initially appear as water-soaked areas, later become yellow, and eventually form dark brown centers as the nematodes continue to feed. After extended feeding, the adult female deposits eggs inside the cortical tissue or near the root surface if they had already moved out of the root to complete their life cycle (Zunke 1990; Linsell 2013; Smiley 2015). The formation of lesions and death of plant cells during extended feeding reduces the amount of functional root length, and restricts adequate water and nutrient uptake for different plants; the lesion can also create suitable conditions for infection by other microbial pathogens in the soil (Smiley 2015). P. penetrans is implicated in replant problems in several kinds of temperate woody fruit crops, 18 such as apple, peach, cherry, grapes and others (Forge et al. 2008; Pokharel et al. 2015; Forge et al. 2016). Despite severe inhibition of productivity, host plants can develop resistance to or tolerate plant parasitic nematodes if plant nutrition and vegetative growth are improved in various ways (Calvet et al. 1995; Hol and Cook 2005). For example, the population of root-lesion nematodes was reduced in AMF-colonized roots in rootstocks that had higher growth and leaf nutrient contents in apples and other temperate fruits (Forge et al. 2001; Hol and Cook 2005). There are only a few reviews reporting use of organic soil amendments, such as compost, green manure and mulch to manage plant parasitic nematodes (Akhtar and Malik, 2000; Thoden et al., 2011; Forge et al., 2016). Organic amendments appear to suppress plant parasitic nematodes by improving soil structure and fertility, altering the level of plant-resistance, and releasing nemato-toxic compounds and free-living microbes that are antagonistic to plant parasitic nematodes (Oka 2010; Forge et al. 2016). A recent finding also showed that compost-amended soil suppressed P. penetrans populations compared to that of non-amended soil in sweet cherry trees in the Okanagan Valley (Watson et al. 2017). However, the appropriate and sustainable control/preventive mechanisms for root-lesion nematodes, particularly P. penetrans, in most temperate fruit trees are yet to be developed. In addition, little is known about the abundance of P. penetrans on non-fumigated and newly established orchards as well as the effect of organic amendment and reducing irrigation water after fruit harvest on suppressing nematodes in sweet cherry trees in the Okanagan Valley. Nematodes can be extracted from soil or plant roots using different extraction methods. For example, Baermann funnel, petri-plate, funnel spray and blender centrifugal flotation are commonly used methods to extract active and/or inactive nematodes from plant materials (Van Bezooijen and Bezooijen 2006; Batista 2013; Ravichandra 2014). In addition, the sucrose centrifugation (Viglierchio and Schmitt, 1983) and Baermann pan technique (Hackenberg et al., 19 2000) have been frequently used to isolate nematodes from soil and root samples. The sucrose centrifugation technique isolates both active and inactive nematodes within a short period of time. Even though it takes longer, in my research I used the Baermann pan/funnel technique because it isolates only active nematodes. 1.6 Fruit quality The quality of fruit refers to how much the end users value a product (Correia et al. 2017; Kyriacou and Rouphael 2018). It is very important for growers as it determines marketable yield. Consumers define fruit quality based on the combination of several quality attributes that are related to physicochemical properties, such as color, firmness, soluble solid concentrations (SSC) and titratable acidity (TA) (Correia et al. 2017).In addition to these quantitative indicators, sensory quality of fruits can be evaluated by a panel of trained judges (Vallverdu et al., 2012). In fresh sweet cherries, fruit skin color is the most important trait (Esti et al. 2002; Serrano et al. 2005; Franci et al. 2008; Correia et al. 2017). For example, a cultivar with 'dark red blush' color was preferred by consumers compared to 'full bright red' cherries (Pérez-Sánchez et al. 2010). Change in fruit color can occur during maturation due to accumulation of anthocyanin in the fruit (Serrano et al. 2009), and during the ripening process because of ABA, which triggers ethylene levels to facilitate fruit ripening (Ren et al. 2011). Fruit color is also affected by the type of cultivar and storage conditions (Esti et al. 2002; Pérez-Sánchez et al. 2010; Correia et al. 2017). Firmness is another important fruit quality trait, serving as a potential indicator of shelf-life and fruit texture (Plocharski et al. 2000). It influences consumer acceptability as the softening and texture changes in sweet cherries during storage modify their organoleptic (senses including taste, sight, smell, and touch) qualities (Esti et al. 2002; Correia et al. 2017). 20 Similar to color, fruit firmness depends on cultivar; for example, late season cultivars are firm compared to early season cultivars (Usenik et al. 2005). The level of fruit firmness slowly decreases after harvest due to high activity of oxidative and pectic enzymes (e.g., peroxidase, polyphenoloxidase, pectin methylesterase and polygalacturonase) causing damage to cell wall components (Remon et al. 2003). Cherry fruit sweetness and sourness are among the parameters determining its consumer acceptance and market values. Fruit sweetness is positively associated with SSC, because it measures sugars such as glucose, fructose, sorbitol and sucrose. By contrast, titratable acidity (TA) (sourness indicator) measures organic acids including malic, citric, shikimic and fumaric acids (Serrano et al. 2005; Franci et al. 2008). Both SSC and TA quality indicators can vary with cultivar and geographic locations where cherries are produced (Correia et al. 2017). Overall, SSC, TA and fruit weight provide very useful information to the growers during the ripening process of sweet cherries (Pérez-Sánchez et al. 2010). Despite variations in sweet cherry quality parameters depending on cultivar, production area and orchard management, some countries have set overall standards that must be met in order to market fruit domestically or to be exported. For example, Cherry Growers Australia (CGA) has set fruit quality standards to classify fruits into domestic and export finest grades (Table 1, ANON 2016). Additionally, these fruit quality traits can be measured using different advanced and expensive techniques, such as spectroscopy, multispectral imaging and hyper spectral imaging (Wang et al. 2015). However, Wang et al. (2015) pointed out that the traditional approaches are preferred over these techniques due to their simplicity, low cost, and applicability to orchardists. Therefore, in my research, I used the traditional method to determine sweet cherry fruit quality. 21 Table 1.1 Fruit quality standards [skin color rating (from 1â light red to 5 â very dark red), firmness, stem pull force, soluble solid concentration (SSC)] for domestic and export market of sweet cherry in Australia (ANON 2016). Quality parameters For domestic For export Rate of fruit skin color 3-5 3-5 Firmness (g/mm2) â¥ 250 â¥ 300 Stem pull force (g) â¥ 500 â¥ 500 Soluble solid concentration (SSC) (Brix°) â¥16 â¥ 17 1.7 Background of the thesis Previously, the commercial cultivation of sweet cherries was limited to the southern and lower altitude areas of the Okanagan Valley, due to sensitivity to spring frosts and erratic rains which are less frequent in the south Okanagan than in the north (Quamme et al. 2010). However, over the last few years, cultivation has expanded to new areas of the North Okanagan, including areas at higher altitudes and farther away from the protective influence of the lake, because climate change has created suitable conditions for cherry production. Specifically, the Northern Okanagan has shown a warming trend, increasing frost free days and a longer growing season, which could increase productivity of fruit trees (Neilsen et al. 2017). Although new cherry orchards have been established in the North Okanagan and at higher elevation areas in the Central and South Okanagan, the availability of adequate and regular water supply is a major concern. Water is becoming more limited due to warmer conditions and competition for fresh water between crop production and a rapidly growing urban population (Neilsen et al. 2006; Neilsen et al. 2016). Based on the current and predicted future trends of climate change in these areas (Neilsen et al. 2017), there may be a requirement for stored water earlier during the growing season. Most of these new orchards are being planted into land that was previously either uncultivated or was used for forage production. The trees planted into these orchards will likely be influenced by soil microbial communities that are different from those that have developed in older orchard soils. An assemblage of plant pathogenic fungi and plant parasitic nematodes that 22 are collectively referred to as the replant disease complex is typically present in older orchard soils. This replant disease complex can have significant negative effects on growth of young trees replanted into old orchard soil, and historically growers would treat the soil with broad spectrum fumigants to suppress the pathogenic organisms before replanting. In addition, soil microbes have both direct and indirect influences on the growth and productivity of tree fruits by manipulating hormonal signals of plants, suppressing plant pathogens, and increasing bioavailability of soil nutrients (Peruzzi et al. 2017; Mercado-Blanco et al. 2018). However, soil biological properties in these new cropping areas have yet to be considered as factors affecting productivity of sweet cherries. Soil fumigants used to be commonly applied by commercial growers to inhibit replant disease in orchards. However, some newly-established sweet cherry orchards are located on soils that have never been treated with fumigants. These new orchards provide an opportunity to study how organic amendments and deficit irrigation affect diverse native soil microbial communities. Ultimately, this could lead to an understanding of their impacts on long-term soil health, ability to suppress soil pathogens, compatibility with sweet cherry trees, and effects on fruit yield and quality. 1.8 Research objectives and hypotheses The project was proposed to investigate the influence of post-harvest deficit irrigation and organic mulches on soil health indicators and the water potential, productivity, and fruit quality of sweet cherry trees in two newly- established and one old orchard in the Okanagan Valley over two growing seasons. To accomplish this goal, biweekly measurements of stem water potential, and an annual measurement of trunk diameter and analysis of yield and fruit quality were conducted in 2016 and 2017. Soil and root samples were also collected and analyzed for soil chemical and biological properties and root colonization by AMF over the two growing seasons. This study tested the following hypotheses: 23 I. Stem water potential will be similar between sweet cherry trees growing under postharvest deficit irrigation and full irrigation. The rationale for this hypothesis was that a reduction of irrigation water by 40- 70% resulted in similar/ or slightly lower stem water potential in apple and plum trees (Samperio et al. 2015; Neilsen et al. 2016). Therefore, I predicted that a reduction of irrigation water up to 28% during a drought-tolerant phenological stage (after fruit harvest) would not affect the water status. II. Productivity and fruit quality of sweet cherry will not be affected by postharvest deficit irrigation compared with full irrigation (control). Reduction of irrigation water up to 70% after harvest did not affect tree growth, and fruit yield and quality of plum in Spain (Samperio et al. 2015). III. Orchard soils mulched with compost or woodchips will have more organic matter, higher microbial abundance and greater microbial enzyme activity compared to bare soils. In previous studies, addition of compost to orchard soils has enhanced soil nutrients, organic matter, microbial abundance and soil microbial activities (García-Ruiz et al., 2012; Neilsen et al., 2014). IV. Mulches of woodchips or compost will increase soil moisture content compared to bare soil. Different kinds of mulches have been known to maintain soil moisture content in different agricultural fields (Kader et al. 2017). V. There will be a higher rate of root colonization by arbuscular mycorrhizal fungi in bare soil than with surface-applied compost. High P content in soil has a negative effect on AMF root colonization and application of compost is expected to supply considerable N and P to the plant (Cavagnaro 2015). Therefore, I predicted that the amount of P released from compost would be high enough to suppress colonization by AMF. VI. Population densities of P. penetrans will be lower in organically-amended than bare soils. Organic amendments stimulate other beneficial microbes in the soil by supplying the 24 required nutrients and that can lead to direct or indirect suppressive effects on plant-parasitic nematodes (Forge et al. 2003; Kimpinski et al. 2003). Another specific objective was to determine effects of the historical use of woodchips mulch and phosphorus fertigation (10 years preceding replanting), and preplant fumigation and incorporation of compost on soil chemical and biological factors, and early growth and physiological performance of trees replanted into an old sweet cherry orchard harboring the replant disease complex (SuRDC). Similar to the other sites, soil samples were collected over two growing seasons, and different variables, such as P. penetrans from soil and root samples, total microbial abundance, microbial activity, AMF colonization and soil nutrients were analyzed. Given that, the following hypotheses were tested: I. Due to the significant effect of fumigation on living organisms (Dangi et al., 2017), the P. penetrans population, microbial activity, microbial abundance and AMF colonization will be lower in fumigated than non-fumigated soil. II. Compost-amended plots and plots mulched with wood chips for 10 years prior to replanting will have higher microbial activities, microbial abundance and soil nutrient contents than non-treated plots. My rationale is that mulch decomposes after a few years, and starts to release its nutrients, which would be available for trees and used by microbes in soils (García-Ruiz et al., 2012). III. Plots that received phosphorus fertigation for 10 years before replanting will have higher phosphorus content in soil than non-P-fertigated plot soils. 25 Chapter 2: Materials and Methods 2.1 Description of the experimental sites This thesis research is part of a larger study conducted on four experimental sites in the Okanagan Valley, BC: two orchards planted to fruit trees for the first time (Coldstream and Lavington) and two previously established orchards (Dendy, and Summerland Research and Development Center (SuRDC)) (Table 2.1). The first site, Coldstream (Site1), had been pasture land, and was converted to a sweet cherry orchard in spring 2015. The second site, Lavington (Site 2), was a dairy farm before its conversion in spring 2014. Unlike the new orchards, the Dendy (Site 3) orchard was used for the production of apples before it was planted with sweet cherry trees in spring 2013. The SuRDC (Site 4) orchard was replanted with new cherry trees in spring 2015 on soil formerly cropped with sweet cherry. Trees of four different sweet cherry varieties grafted on three well-known rootstocks were planted at the four experimental sites (Table 2.1). At Site 1, Staccato scions grafted on Mazzard rootstock were planted. Staccato is a very late cherry variety that produces large, firm and rain-induced cracking-tolerant fruits, while Mazzard is vigorous and the most preferred rootstock in wet and heavy soils due to its fibrous root system (Long and Kaiser 2010). Trees of Sentennial scions grafted on Mazzard rootstock, were planted at Site 3. Sentennial trees produce heavy crops and are known by their growth habit with flat angled and well-spaced branches. They also produce fruits with very firm, crunchy and moderately sweet flesh. At Site 2, trees with Skeena scions grafted onto Gisela 6 rootstock were planted. Skeena is a productive and self-fertile variety that produces very firm and rain-induced cracking-tolerant fruits. Gisela 6 is a semi-dwarf rootstock (relative to Mazzard) with very open and round tree structure, and is well suited for heavy soil types. A semi-dwarf rootstock, Krymsk 5, grafted with Lapin scions was planted at Site 4. Krymsk 5 produces trees that are 20 - 30% smaller 26 compared to those grown on Mazzard, and is compatible in grafting with all cherries varieties. Lapin is a mid/ late season variety that has trees with vigorous growth and upright growth habit. It also produces fruit that has a good tolerance to rain-induced cracking. Generally, in terms of fruit ripening, Skeena on Gisela 6 rootstock at Site 2 has a similar fruit ripening period to Lapins on Krymsk 5 at Site 4, while Sentennial on Mazzard at Site 3 shares a similar fruit ripening period to Stacatto on Mazzard at Site 1 (Tom Forge pers.comm.). Table 2.1 Detailed description of experimental sites and planting materials. Sites Location Soil type Cherry trees (grafted ) Irrigation system Spacing Scion# Rootstock Between trees (m) Between rows (m) 1 50° 14'N 119° 8'W Sandy loam 'Stacatto' Mazzard (P. avium) Drip 2.2 4.5 2 50° 14'N 119° 7'W Sandy loam 'Skeena' Gisela 6 (P. cerasus x P. canescens Drip 2.4 4.5 3 49° 51'N 119° 23'W Loamy sand 'Sentennial' Mazzard (P. avium) Micro sprinkler 2.7 4.8 4 49° 33'N 119° 38'W Loamy sand 'Lapins' Krymsk 5 (P. fruticosa x P. lannesiana) drip 1.5 4 Sites: 1= Coldstream, 2= Lavington, 3= Dendy, 4= Dendy and Summerland Research and Development Center (SuRDC); #Scion (Prunus avium L.) Different irrigation and fertigation regimes were followed in every experimental site (Table 2.1). The sweet cherry trees at Sites 1 and 2 were irrigated daily during the growing season and less frequently after harvest depending on weather conditions through 2 L h-1 drip irrigation systems. In the 2016 growing season, trees were fertigated with 163 g N tree-1 as calcium nitrate three times from mid-May to the end of June; 285 g N tree-1as urea at the end of March; and 272 g tree-1of N-P-K (20-20-20) blend in mid-May. Two applications of magnesium sulphate were applied at a rate of 0.95 g tree-1of Mg in May. At Site 3, trees were irrigated a total of 8-9 hrs week-1 during the growing season and 4-5 hrs week-1 after harvest using 72 L h-1 27 micro sprinkler irrigation systems. In both growing seasons, the following fertilizers were applied: 150 g N tree-1 as calcium nitrate twice between mid-May and the end of June; 250 g tree-1 of N-P-K (20-20-20) blend in mid-May; and 0.9 g tree-1of Mg (as magnesium sulphate) in May. Irrigation at Site 4 was applied through a 4 L h-1 drip irrigation system. The daily irrigation duration was scheduled based on evaporation demand of the trees in the previous day as measured by an atmometer (ET Gauge Co., Loveland, CO). Trees were fertigated in June 2016 and 2017 with 20 g P tree-1(as phosphoric acid), 35 g N tree-1 (as calcium nitrate), and 20 g K tree-1 (as K-Mag® potassium fertilizer, Mosaic). 2.2 Experimental design and treatments Split-plot experiments with a randomized complete block design were established at Site 1 (Coldstream), Site 2 (Lavington) and Site 3 (Dendy), with two irrigation treatments assigned to main plot units and three soil amendments assigned to subplot units (2 x 3 = 6 treatments). Each subplot unit included four trees (two measurement and two guard trees). There were six randomly-assigned replicate rows and a guard row at each side of the experimental row on all of the experimental sites, resulting in 6 treatments x 6 replicate rows = 36 plots. The irrigation treatments included full irrigation (100%) and post-harvest deficit irrigation (a reduction of 25% at Site 1 and 2 and 28% at Site 3 of the full irrigation). At Sites 1 and 2, full irrigation was applied using driplines with a spacing of 18 inches between emitters at a rate of 1 L per hour (LPH), whereas post-harvest deficit irrigation was applied using driplines that contains 24 inches spacing between emitters, allowing a reduction of 25% applied water. At Site 3, micro sprinkler heads with the rate of 72 LPH were used for full irrigation, whereas micro sprinkler heads with a rate of 52 LPH were installed for PDI to reduce applied water by 28%. Postharvest deficit irrigation was started after harvesting cherry fruits in 2016 at Sites 2 and 3 and in 2017 at Site 1. The total evapotranspiration rates between May and October were 719 mm in 2016 and 776 28 mm in 2017 at Sites 1 and 2, and 728 mm in 2016 and 815 mm in 2017 at Site 3 (http://www.farm west.com/climate/et). The percentage of total available water (TAW) content was determined from volumetric soil moisture content (SM) (%) using a formula expressed as: TAW = (SM â PWP) / (FC â PWP) *100, where soil moisture content for sandy soils at the permanent wilting point (PWP) is 15 bars, 8% soil moisture, and at the field capacity (FC) is 0.1 bars, 28% soil moisture (Ebel et al., 2001). Mulch treatments comprised of compost, woodchips and bare soil (no amendment). Each year, GlenGrow compost made from yard waste (Figure 1a, Table 2.2) was obtained from the Glenmore Landfill, Kelowna, BC. Douglas-fir wood and bark chips mulch (Figure 1b) was purchased from Pryce Landscape Product, Vernon, BC for Sites 1 and 2, and from Better Earth Garden Centre, Kelowna, BC for Site 3. Both organic materials were applied in spring to the surface of the soil to 1.5 m wide band centered on the tree row to a depth of 0.05 m. The experiment at Site 4 (SuRDC) was laid out as a split-split-plot design, with six randomized complete blocks. It consisted of fumigation (fumigated and non-fumigated) as main plots, compost amendment as sub-plots (compost and non-compost) and legacy effect treatments (P-fertigation, mulch, no treatment) as sub-sub-plots. Each sub-sub-plot contained five trees (three experimental and two guard trees). âBigHornâ compost (BigHorn Contracting Ltd., Okanagan Falls, BC) â made from beef feedlot waste and grape pomace â was incorporated into the soil prior to planting in March of 2015. 29 Table 2.2 Chemical composition of compost and woodchips used as organic materials. Compositions Glengrow Compost1 BigHorn Compost2 Pryce Landscape Mulch3 Better Earth Mulch3 Total N (%) 1.45 1.4 0.42 0.34 Total Carbon (%) C/N NA 16:1 NA 15:1 47.92 120:1 47.06 157:1 Available P (%) 0.500 0.053 0.036 0.040 Organic matter (%) 43.54 30.2 NA NA EC (mS cm-1) 1.3 3.1 NA NA pH Potassium (%) Zinc(mg kg-1) 8.4 1.10 133.5 7.4 0.56 NA NA 0.06 158.5 NA 0.06 56.5 1= Data provided by city of Kelowna; 2= Data from a single sample of compost; 3 = Data from a single sample of each type of mulch; samples provided by Pryce Landscape Products and Better Earth Gardens and Tropicals. Figure 2.1 Images of the compost (a) and woodchips (b) used as soil organic amendment at Site1 and 2 sweet cherry orchards. Soil moisture and temperature probes (5TM or 5TE probes; Decagon Devices, Pullman, WA) were installed at all experimental sites, except Site 2. Plots at Site 2 received only soil moisture probes (EC-5 probes; Decagon Devices, Pullman, WA). The probes were installed at a depth of 30 cm between the two experimental trees at all sites. The probes were placed in both irrigation treatments at three of the six plots at three sites, whereas at Site 4 the probes were installed in every plot. Automated data loggers (Em50 or Em5b loggers; Decagon Devices, 30 Pullman, WA) were set to record soil moisture and temperature every four hours and the data were manually downloaded biweekly. 2.3 Analysis of water potential, growth, nutrients and fruit quality of sweet cherry trees 2.3.1 Stem water potential (Ψstem) To evaluate the water status of the trees, midday (10 am - 12 pm) Ψstem was measured from two leaves located on the shaded side of each tree using a Scholander pressure chamber (Model 3005: Soil moisture Equipment Corp., Santa Barbara, CA, USA). Four out of six replicates in each site were randomly selected and measured once every two weeks. Two leaves per tree (two trees per plot) were wrapped with black plastic and aluminum foil while attached to the tree to reduce the effect of transpiration and left for an hour to allow equilibration with the stem before excision. The leaves with their petioles were then detached from the trees. The petioles were cut at an angle with a razor blade, rapidly placed in the pressure chamber and pressurized with compressed nitrogen gas until the cut surface became wet and shiny. The pressure was then quickly turned off, and the pressure indicated on the gauge was recorded as MPa. The midday Ψstem of cherry trees was evaluated after implementation of organic amendments and PDI in both growing seasons for Sites 2 and 3, but only for 2017 for Site 1 because PDI was not established at this site until August 2017. 2.3.2 Tree trunk cross sectional area and leaf area Tree trunk cross sectional area (TCSA) and leaf area are important indicators of overall plant vigour. Annual measurements of trunk diameter using a digital caliper were made during the dormant season (November 2016 and 2017) at a permanently marked height, which was 0.3 m above the graft union. 31 To determine the total leaf area per plot, samples comprised of 25 leaves per plot were collected from the mid-third portion of extension shoots of the current-yearâs growth at the standard mid-summer sampling period (mid-July to early August). Total leaf area was then measured from 20 sub-sampled leaves with a leaf area meter (LI-3000, LI-COR Inc.). 2.3.3 Foliar nutrient content The nutrient content of cherry foliage was determined from the leaf samples collected from each plot in three sites (Site 1, 2 and 3) in August. The samples were oven-dried at 65 °C for 24 h and then ground in a mini Wiley mill (Thomas Scientific), and sent to the BC Ministry of Environment Analytical Laboratory, Victoria for analysis. For the analysis of total N, C and S, ~20 mg plot-1 of ground leaf tissue were analyzed using a Thermo Scientific âFlash 2000â combustion elemental analyzer. For the analysis of the rest of the nutrients (Ca, P, K, Mg, Al, B, Cu, Fe, Mn and Zn), the samples (250 mg plot-1) were digested at high pressure and temperature in 4 mL of concentrated nitric acid using a Milestone Inc. âUltrawaveâ SRC digestor. The digested samples were then diluted to 15 mL with 12% hydrochloric acid and analyzed using a dual-view ICP spectrometer (Prodigy, Teledyne / Leeman, Hudson, New Hampshire) (Kalra, 1998 and pers. comm. with Clive Dawson, BC Ministry of Enviroment Laboratory, 2016). 2.3.4 Fruit quality analysis During the commercial harvest period, a randomly-selected 100-fruit subsample from each plot was harvested to determine average fruit weight, the number of cherries exhibiting split skin and other quality analysis methods as described by Neilsen et al. (2014). In brief, out of the 100 fruit subsamples, 20 split-free fruit plot-1 were selected to measure fruit firmness using a FirmTech II (Bioworks, Stillwater, OK) instrument, stem pull force using a Dart FGV-5X 32 digital force gauge, and fruit color using the Michigan State University 1 to 5 scale chart (Michigan State University, East Lansing, MI). Then, the fruit was juiced by mashing in a plastic bag and squeezed out by hand though cheese cloth. Soluble solids concentration (SSC) was determined by placing 1-2 drops of juice on a digital refractometer (Model PR-101; AD Scientific Instruments, Keene, NM). Titratable acidity (TA) was determined by auto titration of the juice to an end point pH of 8.1 (Model 719S Titrino, Herisau, Switzerland). 2.4 Analysis of soil and root biochemical properties 2.4.1 Soil and root sampling and preparation Soil samples of 2-cm diameter and 30 cm deep were collected each year in October from all of the experimental sites after compost and woodchips had been scraped away from the surface of the treated soil. Four soil cores, two cores from close to the trunk and two cores 30 cm away from the trunk were sampled from each measurement tree of each plot from Sites 1, 2 and 3. Three soil cores that were 30 cm away from the trunk were sampled randomly from each measurement tree of each plot from Site 4. The composite soil sample from each plot was placed in a plastic bag, kept in a cooler and then brought to the lab. Immediately, the field moist samples were sieved through a 5.6-mm sieve and stored at 4oC for later nematode extraction, and a portion of these sieved-samples was then sieved with a 2-mm sieve to separate it from fine roots. Then, each fresh soil sample was subdivided, with one sample stored at 4oC for FDA analysis, and a second at -20oC for DNA extraction. Another portion of each soil sample was air dried for 48 h, sieved through a 1-mm sieve, and stored at room temperature for subsequent analysis of soil minerals, POXC, EC and pH analysis. On the same day, fine roots with a total length of approximately 20 cm were collected from each treatment tree at a distance of 30 cm from the trunk and a depth of 5-30 cm from the surface of the soil after removing compost or mulch using a hand trowel. Then, roots were 33 washed in the lab to remove attached soil, and stored in 30 % ethanol at 4 oC until further analysis. 2.4.2 Soil physicochemical property analysis To determine gravimetric water content (GWC), 1 g of collected soil sample was placed into a pre-weighed tin and dried at 105oC for 24-48 h, then used for GWC analysis. The percentage of water was calculated using fresh soil weight (measured on the day of sampling; Ww) and dry soil weight (Wd) of sampled soils: (Ww - Wd)/ Wd). To measure electrical conductivity (EC), a 10-g subsample per plot was suspended in water with a 1:2 soil:water ratio. Then, EC was measured using an EC meter (WTW inoLab Cond 7200). Soil pH was measured from the same suspension used for the EC measurement using a pH meter (Symphony, model B10P). Air-dried (see soil and root sampling and preparation section 2.4.1), ground and sieved (1-mm) soil samples from each site were sent to the BC Ministry of Environment Analytical Lab, Victoria, Canada for nutrient analyses. Among the major nutrients, total C and N were determined from 30-50 mg finely ground soil plot-1 using a Thermo Scientific âFlash 2000â combustion elemental analyzer. Inorganic C was analyzed with a Leco Truspec combustion elemental analyzer, whereas a Ol-Analytical "Aurora 1030W" was used to determine dissolved organic C from samples. Available N and P were determined using 2 MKCl and Bray P1 (0.025 NHCl and 0.03 N NH4F) extractants, respectively, and analyzed colourimetrically using an OI-Analytical âAlpkem FSIVâ segmented flow auto-analyzer and an Agilent Cary-60 UV/visible spectrophotometer, respectively. Cation exchange capacity (CEC) was analyzed on a Teledyne â Leeman Labs âProdigyâ dual view ICP-OES instrument. Soil organic matter was determined by loss on ignition (Jones and Willett, 2006; Kalra, 1998; Kalra and Maynard, 1991). 34 Permanganat-oxidizable carbon (POXC) was used as an index of active carbon in soil via a method adapted from Culman et al., (2012). A 2.5 g of air dried soil sample per plot was weighed and placed into a 50-mL centrifuge tube. Then, 18 mL of deionized water and 2 ml of 0.2 M KMnO4 (potassium permanganate) stock solution were added to each tube before it was shaken by hand for 2 s. Then tubes were shaken on a horizontal shaker (VWR, model 5000) at 240 rpm for 2 min, and kept in the dark for 10 minutes to settle. From each tube, 0.5 mL of supernatant was quickly transferred to a new tube containing 49.5 mL of water and mixed by hand shaking. A 200-μL aliquot of each sample, deionized water (blank reference), soil standard (known soil sample reference), standard solutions (solution quality control reference) and four standard concentrations (0.005, 0.01, 0.015, and 0.02 MKMnO4) were loaded in triplicate into a flat-bottom 96-well plate (Greiner bio-one). Sample absorbance was read at 550nm with a plate reader (Thermo Scientificâ¢ Varioskanâ¢ LUX) using SkanItâ¢ Softwarefor Microplate readers (Thermo Scientific â¢). The amount of active carbon oxidized by KMnO4 was calculated using the following formula; POXC (mg kg -1) = [0.02 mol L-1 â (a + b Ã Abs)] Ã (9000 mg C mol-1) Ã (0.02 L solution Wt-1), Where: ï· 0.02 mol L-1 = initial solution concentration ï· a = intercept of the standard curve ï· b = slope of the standard curve ï· Abs = absorbance of unknown ï· 9000 = milligrams of carbon oxidized by 1 mole of MnO4 changing from Mn7+ to Mn2+ ï· 0.02 L = volume of stock solution reacted ï· Wt = weight of air-dried soil sample in kg 35 2.4.3 Soil microbial activity To get an index of total activity of soil microbes, fluorescein diacetate (FDA) hydrolysis was measured using procedures described by Green et al., (2006) and Zhai et al., (2009). One gram (fresh weight) of soil sample from each plot with three replicates was mixed with 50 ml sodium phosphate buffer (60 mM Na3PO4. 12H2O, pH 7.6; Sigma-Aldrich) in 50-mL centrifuge tubes. Colourless FDA (0.5 ml of 4.9 mM) was then added to the solution. Similarly, two blank controls (no FDA and no sample soil) were prepared. The tubes were then incubated at 37 oC in an incubator (Thermo Scientific, Max Q 50000) for three hours. After three hours of incubation, the reactions were immediately stopped by adding 2 mL acetone (> 99.5%; Sigma-Aldrich) and mixing thoroughly by inverting 3-5 times. The tubes were then centrifuged at 1725 rpm for 5 min, and 1 mL of supernatant from each sample was transferred into a 1-mL cuvette. Then, the fluorescein released was measured by spectrophotometry (Thermo Scientific Genesys 20 Spectrophotometer Model 4001/4) at excitation and emission wavelengths of 490 and 515 nm, respectively. A linear standard curve was developed from different concentrations of commercial fluorescein (0.03, 0.1, 0.3, and 0.5 mg). When the absorbance of the sample was above the range of the standards, the assay solution was diluted with sodium phosphate buffer (pH 7.6) until the absorbance was obtained within the range of absorbance of the standards. The resulting absorbance values were converted to concentration of fluorescein produced (μgml-1 of solution) using the standard curve. For each assay, fluorescein (μg) produced in solution was then converted to fluorescein (μg) per gram of soil sample used. 2.4.4 Extraction of nematodes from soils and roots In October 2016 and 2017, nematodes were extracted from soil and root samples within two weeks after sampling from each experimental site. For samples from Sites 1,2 and 3, 50 g (fresh weight) of soil sample per plot (see section 2.4.1 for soil and root sampling) were 36 prepared for nematode extraction using the Baermann pan technique (Hackenberg et al., 2000). Soil samples were placed on nylon meshes (0.5 mm) that were covered with tissue papers and each placed on a 20-cm diameter plastic pan. Then, the pan was filled with water until it covered the soil sample and incubated for seven days. After seven days, the water from the pan was poured onto a No. 500 sieve (25μm), which was kept at a 45° angle to allow the water to filter through while keeping nematodes at the bottom wedge of the angled sieve. Extracted nematodes were transferred into 20-mL scintillation vials, after washing the extracted nematodes with water. Nematodes were also extracted from 100 g soil samples from each plot in Site 4 using the sucrose centrifugation technique (Prot et al. 1993 and references therein). Soil (100 g) was mixed with 1.8 L tap water in a 2-L pitcher and the mixture was allowed to settle for 30 s. The supernatant was then poured through a No. 35 (447μm) sieve. Then, the contents of the first pitcher were immediately poured over a No. 400 (38μm) sieve kept at a 45° angle. The remaining nematode-containing residue on the sieve was funneled into a 50-mL centrifuge tube that was then centrifuged for 5 min at 3000 rpm. After decanting off the supernatant, the tube was filled with sucrose solution (454 g sucrose L-1 water) and shaken until the soil pellet was resuspended. The tube was then centrifuged for 1 min at 3000 rpm, decanted onto a No. 500 sieve, and nematode samples rinsed and poured into 20-mL scintillation vials. Fine roots (< 2 mm diameter) that had been separated from soil through the sieve (2 mm) and cut from root samples were used for endoparasitic nematode (P. penetrans) extraction using the Petri-plate technique (Ravichandra, 2014). Fine root subsamples (~ 2 g) were placed into plastic Petri plates filled with water. After seven days of incubation at room temperature, the water with extracted nematodes was poured onto a No. 500 sieve, and nematodes were transferred into 20-mL scintillation vials. Root subsamples from the extraction were left to dry at room temperature for 72 h to determine root dry weight. 37 Extracted nematodes from the soil and root samples were kept in water and stored at 4oC until the nematode counting was completed (approximately two and half months). Pratylenchus penetrans, root-lesion nematodes, in each sample were identified and counted in a gridded counting dish under an inverted microscope (Olympus CK2 Inverted Microscope; 100x magnification); P. penetrans were distinguished from other nematodes based on morphological features, such as lip annules, short stylet with basal knobs, pharynx overlapping the intestine ventrally and rounded tail (Castillo and Vovlas 2007). The data were expressed as the total number of nematodes and the total number of P. penetrans per 50 g of soil for the three primary sites, or 100 g-1 of soil for Site 4. 2.4.5 Root colonization by arbuscular mycorrhizal fungi The density of AMF root colonization was determined from fine root samples collected from all four experimental sites (see section 2.4.1 for soil and root sampling). Roots preserved in 30 % ethanol were cut into approximately 20 x 1 cm fragments, and sandwiched between two layers of cheesecloth, and put into root staining baskets. The baskets were then soaked for 24 h in 10% KOH (Fisher Scientific, Hampton, NH) at room temperature. The overnight KOH solution was replaced by fresh 10% KOH and the roots were heated in the KOH in a 95 °C water bath for 1 h. After removing the KOH, the roots were washed with water and then soaked in 3% H2O2 (Fisher Scientific, Hampton, NH) for 45 min at room temperature. Following a thorough rinse with water, roots were soaked in 5% HCl (VWR®, Radnor, PA) for 5 min at room temperature. Roots were then soaked in 0.001 % aqueous Trypan Blue for 24 h at room temperature (Vierheilig et al. 2005). Stained roots were mounted with polyvinyl alcohol-lactic acid-glycerol gel (PVLG) (VWR®, Radnor, PA) on a microscope slide and covered with a cover slip. Mounted slides were then viewed using a compound microscope (Nikon Eclipse E800) at 200x magnification and 38 colonization quantified using the magnified intersection method (McGonigle et al., 1990). The presence of arbuscules, mycorrhizal hyphae only, or vesicles was determined at each intersection (field of view).Then, the percentage of AMF colonization was calculated using the following formula: AMF colonization (%) = (the number of intersections with mycorrhizal structures / the total number of intersections)*100. 2.4.6 Quantification of total soil fungi and bacteria The total abundances of bacteria and fungi in the soil were quantified using quantitative PCR (qPCR). Total DNA was first isolated from 0.25-g frozen soil samples from each plot using a PowerSoil DNA Isolation Kit (Mo Bio Laboratories, Carlsbad, CA). DNA concentrations were determined using a Nanodrop spectrophotometer (Thermo Scientific, Wilmington, DE), then the DNA was aliquoted into five cryovials (~20μL per cryovial) and stored at -80 CÌ until needed. For the standard curves, known plasmid DNAs carrying coding sequences for the fungal 18S rRNA (pJ201-F1-1) and for bacterial 16S rRNA (pJ201-16S), which were synthesized by Tristan Watson (Watson 2018) and DNA 2.0 (Menlo Park, CA), respectively, were used. The concentration of the plasmids was determined using a NanoDrop spectrophotometer (Thermo Scientific, Model: 2000). pJ201-F1-1 and pJ201-16S were linearized using XmaI (1 μL) and EcoRI (1 μL) restriction enzymes (NEB Biolabs, Ipwich, MA), respectively. The linearized plasmids were then purified using QIAquick PCR Purification Kit (QIAGEN, Frederick, MD) and quantified in triplicate using the Nanodrop spectrophotometer. The mean of triplicate Nanodrop-based quantities was used to calculate the amount of solution containing106 copies of the 16S and 18S rRNA regions. The volume calculated for 106 copies of template plasmid DNA was aliquoted into cryovials and stored at -80ºC until needed (Munro, 2017). 39 Isolated DNA samples were diluted 100 to 1000-fold and used as templates for quantification of total bacteria and fungi using qPCR. A 20μL reaction volume containing 10μL of SsoFastâ¢ Evagreen Supermix (Biorad, Hercules,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 diluted DNA sample was prepared in triplicate for each sample. For the standard curve, a 10-μL reaction volume consisting of SsoFastâ¢ Evagreen®Supermix, 1.6 μL of 0.1 μM of appropriate forward and reverse primer, and 5.0 μL of template DNA of a serial dilution (106â101 gene copies per reaction) was prepared. To estimate the total abundance of bacteria in soil, a portion of the 16S rRNA region was quantified using the 16S rRNA-specific primer,BACT1369F (5â-CGGTGAATACGTTCYCGG-3â) and PROK1492R (5â-GGWTACCTTGTTACGACTT-3â) (Suzuki et al. 2000). The qPCR program for bacteria was set as 95 °C for 5 min, with 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. Likewise, a portion of the 18S rRNA region was amplified by qPCR using primer set of FR1 (5â-AICCATTCAATCGGTAIT-3â) and FF390 (5â-CGATAACGAACGAGACCT-3â) (Prévost-Bouré et al., 2011) to determine the abundance of total fungi. The qPCR for this organism was run using a program set as 95 °C for 5 min, 40 cycles of 95 °C for 30 s and 59 °C for 1 min, and one cycle of 70 °C for 60 s. The densities of total bacteria and fungi were determined by comparison of the cycle threshold (CT) â the number of cycles required to detect a signal from sample DNA â to a standard curve generated based on the known plasmid DNA. As the copy number of target genes (i.e., 16S and 18S rRNA) strongly correlates with the proxy biomass of total bacteria and fungi (Thies 2008), the total bacteria and fungi are reported as copy number per gram of dry soil. The amount of dry soil used for isolation of DNA was calculated using the formula: dry soil (g) = wet soil (g) used in DNA isolation * (100 - GWC (%)). Ultimately, the copy number for each gene per gram of dry soil was determined using the following formula: copies per gram of dry 40 soil = [(mean of starting quantity * elution factor * dilution factor) / amount of template in qPCR] / dry soil (g) used in DNA isolation. 2.5 Statistical analysis To evaluate the effect of organic amendment treatments over all three sites for the duration of the study, a split-plot experimental design was used, with sites as main plot and mulch treatments as sub-plot. Site and mulch treatments were considered as fixed effects, whereas year-block and year-block*site were considered as random factors. In this analysis, irrigation was not considered because irrigation treatment was started in different years at Sites 2 and 3 (after harvest in 2016) than at Site 1 (after harvest in 2017). Specifically, to determine the effect of organic materials, the data from all plots were used for most of the variables, while the data only from fully irrigated plots were used for fruit quality and yield variables (to avoid any effect of irrigation treatment).The responses of Ψstem to mulch treatments were also examined across three sites over two seasons together using data from only the full irrigation treatment. When evaluating the effect of irrigation treatments on trunk diameter (TCSA), soil chemical and biological factors, data from both years collected from Sites 2 and 3 were analyzed together as a split- split -plot design with site as main plot, irrigation as sub-plot and mulch treatments as sub-sub plot. Site, irrigation, mulch and irrigation x mulch interaction were considered as fixed effects, whereas year-block and year-block*site, year-block*site*irrigation were considered as random factors. In addition, 2016 data from Site 1 (no irrigation treatment) were subjected to a blocked one-way analysis of variance with mulch treatments as a fixed effect. Site 1 data from 2017 (after irrigation treatment) were analyzed as a split-plot design with irrigation and mulch treatments as fixed and block, block*irrigation as random factors. Irrigation effects on fruit quality, leaf area and foliar nutrients were only evaluated for 2017 at Site 2 and 3 41 because those measurements were taken at the time of harvest and, thus, were not affected by irrigation treatment in 2016 because PDI was imposed after harvest. Additionally, the impact of treatments was assessed at each site individually for the duration of the study. At Sites 2 and 3, irrigation and mulch treatments (the main factors of interest) were considered as fixed factors, and year-block (2 years * 6 blocks = 12 combinations) and year-block*irrigation were considered as random factors, according to previous reports depicting a similar experimental design and analysis (Liu et al. 2011; Zheljazkov et al. 2011). At Site 1, only the effect of mulch treatments was evaluated over two years by considering mulch treatments as a fixed factor, and year-block and year-block*mulch as random factors. Whenever an interaction effect of site with mulch treatments or irrigation was detected in soil biological and chemical properties, and fruit quality, analysis of these parameters was performed at each site separately. Irrigation and mulch were considered as fixed factors, and block and block*irrigation were considered as random factors for variables collected after the application of irrigation treatment. In particular, for variables measured prior to the application of irrigation treatments, only mulch treatment (as a fixed factor) and block and block*mulch (as random factors) were considered during the analysis. All the results are presented as a mean of n=12 (2 plots X 6 replicates for each site), except for stem water potential (n=8) and soil moisture content (n= 3-5) for each mulch treatment, while the results for variables evaluated for the effect of irrigation are presented as a mean of n=6, except for stem water potential (n=4) and soil moisture content (n= 2-4). At Site 4 (SuRDC), a split-split-plot design, consisting of two fumigation treatments as a main-plot, compost amendment as a sub-plot and three legacy effects of mulch and P-fertigation as a sub-sub-plot, was used to analyze the data from the two years (2016 and 2017) together. The three factors of interest (fumigation, compost amendment and legacy effect) were 42 considered as fixed effects, and year-block (12 blocks = combination of two years and six blocks), year-block*fumigation, year-block*compost and year-block*fumigation*compost were considered as random effects. The ANOVAs were completed for all parameters using the SAS PROC MIXED procedure (SAS Institute Inc., 2009). Further, means comparison was completed for significant (P < 0.05) and marginally significant (P < 0.1) effects by comparing the least squares mean of the corresponding treatment combinations using Tukeyâs test. Letter groupings were generated using a 5 % level of significance when a main effect or the two-factor interaction was significant. For each response, the validity of model assumptions was verified by examining the residuals as described in Montgomery (2012) and appropriate transformations (e.g., log (x+10)) for abundance of P. penetrans from soil and roots and a log transformation for other variables when it was needed) were applied on responses with violated assumptions. The relationship between stem water potential and volumetric soil moisture was determined by using mixed effect linear model (lmer, Version 1.1.383) in Rstudio. 43 Chapter 3: Results 3.1 Soil moisture content and cherry tree water status at three sites The Okanagan Valley has diverse climatic conditions, with a wide range of temperature and precipitation across specific locations and growing seasons. Our project was located at sites in the north and central Okanagan Valley: Coldstream (Site 1), Lavington (Site 2) and East Kelowna (Site 3), for two years (2016 and 2017). According to monthly summaries of temperature and precipitation from local Environment Canada weather stations at these locations (http://climate.weather.gc.ca/prods_servs/cdn_climate_summary_e.html), 2017 had a drier growing season (early June - early October) compared to 2016. Within 2016, August was the hottest month at all sites, with the average, maximum and minimum temperature ranging from 20 - 21.5 oC, 33.4 - 35.1 oC and 6.9 -10.6 oC, respectively. This month also had the least precipitation (6.4 - 23.4 mm), compared to the wettest month (July), which received 35 - 42.1 mm precipitation in 2016. By contrast, July was the hottest and driest month in 2017, with average, maximum and minimum temperatures of 20.9 - 23.8 oC, 35.1 - 36.9 oC and 6.4 -10.9 oC, respectively, and no precipitation at all sites. June was the wettest month (9.3 - 20 mm) in 2017, but it fell in the range of the driest month (August) in 2016. The water holding capacity of a sandy loam soil (e.g., at Sites 1 and 2) is 12.5 cm m-1 (or 0.125 m3 m-3) and loamy sand (e.g., at Site 3) is 10 cm m-1(or 0.1 m3 m-3) (BC Ministry of Agriculture, 2015). The volumetric soil moisture content varied across three sites from early June to early October in 2016 and 2017. The average daily soil moisture contents at Sites 1 and 2 ranged from 0.1- 0.3 m3 m-3 (total available water (TAW) = 10 -110%) in 2016 and 0.1- 0.6 m3 m-3 in 2017 (TAW= 10 - 260%) (Figures 3.1 - 3.4). Similarly, the volumetric soil moisture content at Site 3 varied from 0.3 - 0.5 m3 m-3 (TAW = 110 - 210%) in 2016 and 0.1-0.4 m3 m-3 (TAW =10 -160%) in 2017 (Figures 3.1 - 3.4). Soil moisture contents remained within a fairly narrow range 44 because of irrigation (Figures A1-A8). The only effects of mulch treatments on average monthly soil moisture were that soils were drier in woodchips amended plots compared to other treatments at Site 1 in 2016, and drier than compost plots at Site 2 in 2017 (Table 3.1). Across three sites over two growing seasons, average monthly minimum soil moisture contents were higher in plots with compost than in non-amended plots, while no effect was observed in woodchips amended plots (Figure 3.5; Figures A1-A8). The response of soil moisture to PDI is presented for only Sites 2 and 3 in 2017 (Table 3.1), due to the problems with data loggers and sensors in 2016 at Sites 2 and 3 and in 2017 for Site 1 (first PDI application). There was no effect of PDI on average monthly and minimum soil moisture contents at either site. 45 Table 3.1 Average monthly mean volumetric soil moisture contents as affected by postharvest deficit irrigation and mulch treatments at Coldstream (Site 1), Lavington (Site 2) and Dendy (Site 3) sweet cherry orchards. Site 1 Site 2ê Site 3ê Mulch treatment Soil moisture (m3m-3) SE Mulch treatment Soil moisture (m3m-3) SE Mulch treatment Soil moisture (m3m-3) SE (2016) Bare 0.21a 0.01 Bare 0.21 0.04 Bare 0.27 0.07 Compost 0.18a 0.01 Compost 0.22 0.03 Compost 0.26 0.07 Woodchips 0.11b 0.01 Woodchips 0.20 0.04 Woodchips 0.27 0.07 ANOVA F P ANOVA F P ANOVA F P Mulch (df=2) 27.85 0.034 Mulch (df=2) 0.03 0.971 Mulch (df=2) 0.1 0.909 Date (df=3) 26.89 <0.001 Date (df=3) 20.87 <0.001 Date (df=3) 9.56 <0.001Date*Mulch (df=6) 2.45 0.053 Date*Mulch (df=6) 0.29 0.938 Date*Mulch (df=6) 0.64 0.699 (2017) Bare 0.14 0.04 Bare 0.19ab 0.02 Bare 0.23 0.02 Compost 0.27 0.04 Compost 0.21a 0.02 Compost 0.23 0.02 Woodchips 0.19 0.04 Woodchips 0.18b 0.02 Woodchips 0.23 0.02 ANOVA F P Irrigation Mulch 2.40 0.207 PDI 0.21 0.02 PDI 0.23 0.02 Date 0.85 0.5524 Full 0.18 0.02 Full 0.23 0.02 Date*Mulch 0.46 0.939 ANOVA F P ANOVA F P Irrig (df=1) 5.98 0.1343 Irrig (df=1) 0.04 0.8637Mulch (df=2) 3.41 0.0374 Mulch(df=2) 0.15 0.8622Irrig*Mulch (df=2) 1.95 0.1478 Irrig*Mulch (df=2) 2.22 0.1233Date (df=8) 4.9 <0.001 Date(df=3) 4.43 0.0094Date*Irrig(df=8) 0.69 0.7025 Date*Irrig(df=3) 0.22 0.8823Date*Mulch (df=16) 1.03 0.4379 Date*Mulch (df=6) 0.22 0.9661Date*Irrig*mulch(df=16) 0.8 0.6809 Date*Irrig*Mulch(df=6) 0.26 0.9505Data were averaged over each month (June-October) for each plots and expressed as mean (n= 2-5 plots) and standard errors (SE); Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. Effects of Mulches and PDI were evaluated at Sites 2 and 3 in 2017, but only Mulches were evaluated at Site 1. Date refers to averaged month. ê PDI were evaluated at Sites 2 and 3 only in 2017 because of data loggers and sensors malfunction that resulted in many missing data from plots in 2016 for Site 2 and 3 and 2017 at Site 1. 46 Figure 3.1 Average daily soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2016 growing season across the three sites. Each point is typically an average of three plots per site per day, except when data were missing because of data logger error. 47 Figure 3.2 Average daily soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2017 growing season across the three sites. Each point is typically an average of four plots per site per day, except when data were missing because of data logger error. 48 Figure 3.3 Average daily soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2016 at Sites 2 and 3. Each point is typically an average of four plots per site per day, except when data were missing because of data logger error. Black arrow indicates the start of PDI. 49 Figure 3.4 Average daily soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2017. PDI- treated plots had partial measurement at Site 1 as PDI was implemented only in 2017 (Aug â Sep). Each point is typically an average of four plots per site per day, except when data were missing because of data logger error. Black arrow indicates the start of PDI. 50 Figure 3.5 Effect of mulches on monthly averaged minimum volumetric soil moisture content across three sites over two seasons. The analysis was conducted for data from fully irrigated plots only and by considering site and month as a random effects and mulch as a fixed effect. Each point is a mean of four plots per treatments per sites except when data were missing because of data logger error. Mulches sharing the same letter do not differ significantly at p<0.05. The stem water potential value across the three sites in both seasons ranged from -2.40 to -0.34 MPa (Figures 3.6 and 3.7).The water potential generally decreased as the summer progressed in 2017, with the lowest values occurring in September at Sites 1 and 2 and in early August at Site 3. Trees also had the lowest values of stem water potential values (highly negative) in late August of 2016 at Site 3. Consistent with the rather limited effects on soil moisture content, neither compost nor woodchips affected the stem water potential across three sites over two seasons, as well as within each site considered separately (Table 3.2). Application of PDI also had no effect on stem water potential over the two growing seasons at Sites 2 and 3, as compared to the full irrigation treatment (Table 3.2). However, there was an interaction effect of irrigation and mulch at Site 2 over two seasons; higher stem water potentials (less negative) were recorded from fully irrigated plots with woodchips relative to bare plots exposed to PDI (Table I1). When we examined the relationship between stem water potential 51 and soil moisture content at each site in each growing season, we found no relationship (Figures B1-B3). Figure 3.6 The patterns of stem water potential (á´ªstem) (MPa) of sweet cherry trees at Sites 2 and 3 in 2016 in response to irrigation treatments. PDI was implemented a few days after fruit harvest (as indicated with black arrow). Each value is the mean of n=4 ± SE. FI = full irrigation and PDI = Postharvest deficit irrigation. 52 Figure 3.7 The patterns of stem water potential (á´ªstem) (MPa) of sweet cherry trees at three sites in 2017 in response to irrigation treatments. PDI was implemented a few days after fruit harvest (as indicated with black arrow). Each value is the mean of n=4 ± SE. FI = full irrigation and PDI = Postharvest deficit irrigation. 53 Table 3.2 The responses of stem water potential (á´ªstem) (MPa) of sweet cherry trees to mulch treatments at three sites and for both mulch and irrigation at each site (1, 2 and 3) over two growing seasons. Three sites á´ªstem (MPa) SE Site 1 á´ªstem (MPa) SE Mulch treatment Mulch treatment Bare -1.09 0.07 Bare -0.94 0.03 Compost -1.09 0.07 Compost -0.90 0.03 Woodchips -1.06 0.07 Woodchips -0.91 0.03 ANOVA F P ANOVA F P Site (df=2) 0.85 0.457 Mulch (df=2) 1.46 0.303 Mulch (df=2) 0.42 0.657 Date (df=10) 73.95 <0.001 Site*Mulch (df=4) 0.33 0.858 Mulch*Date (df=20) 0.36 0.995 Site 2 Site 3 Irrigation (Irrig) Irrigation (Irrig) PDI -1.02 0.04 PDI -1.03 0.03 Full -0.97 0.04 Full -1.00 0.03 Mulch treatment Mulch treatment Bare -1.00 0.04 Bare -1.00 0.03 Compost -1.00 0.04 Compost -1.01 0.03 Woodchips -0.98 0.04 Woodchips -1.02 0.03 ANOVA F P ANOVA F P Irrig(df=1) 3.44 0.160 Irrig(df=1) 0.47 0.542 Mulch(df=2) 0.80 0.449 Mulch(df=2) 0.22 0.801 Irrig*Mulch (df=2) 3.46 0.032 Irrig*Mulch(df=2) 1.32 0.269 Date(df=12) 121.64 <0.001 Date(df=12) 135.9 <0.001 Irrig*Date(df=12) 1.05 0.402 Irrig*Date(df=12) 1.21 0.274 Mulch*Date(df=24) 0.26 0.999 Mulch*Date(df=24) 1.11 0.336 Irrig*Mulch*Date(df=24) 0.59 0.934 Irrig*Mulch*Date(df=24) 0.39 0.995 Ψstem were evaluated after implementation of mulch treatments and PDI in both growing seasons for Sites 2 and 3, but only for 2017 for Site 1 because PDI was not established at this site until August 2017. The responses of Ψstem to mulch were examined across three sites over two seasons together (only using data from full irrigation). Data = mean of n= 8 plots (mulch) and n=4 plots (Irrig)/treatment/site and standard errors (SE). âdateâ refers the measurement days of Ψstem from beginning of June to the end of September. 54 3.2 The response of soil chemical properties to mulches and postharvest deficit irrigation Compost and woodchips changed several soil chemical properties at the three orchards over two years (Tables 3.3 - 3.7). Of these chemical properties, the concentration of POXC (active C), total C, inorganic C, organic C, organic matter, total N, P, K, Mg, Ca, Na, pH, EC, and CEC were higher in compost than those in bare-ground plots. By contrast, the addition of compost reduced soil Al and Mn concentrations compared to unamended soils and did not affect soil C:N ratio. Application of woodchips enhanced soil total C and C:N ratio, but decreased soil NO3, NH4, Na and EC relative to the non-amended soil. None of the other measured soil chemical properties was influenced by woodchips across three orchards over two years. In spite of these general responses, the effect of organic materials on soil chemical properties varied somewhat among sites (Tables C1-C9). Several soil chemical properties were analyzed after PDI implementation at Site 1 in 2017 and at Sites 2 and 3 in 2016 and 2017 (Tables 3.4; 3.6 & 3.7). At Sites 2 and 3, none of the chemical properties was affected by PDI over two seasons, except for a tendency for Al concentrations to be reduced under full irrigation. Specifically higher Al concentrations were observed from plots under PDI and woodchips relative to plots with full irrigation and woodchips (Table I1). Similarly, no effects were observed on the chemical properties at Site 1 in the 2017 growing season, with the exception of soil K concentrations, which were lower under PDI as compared to full irrigation (Table C1). 55 Table 3.3 Impact of mulch on different forms of carbon including C (total carbon), POXC, OrgC (organic C), InoC (inorganic C) and on OM (organic matter), C:N ratio and Mn. Mulch treatment C (%) SE POXC (mg kg-1) SE OrgC (mg kg-1) SE InoC (%) SE OM (%) SE C:N SE Mn (mg kg-1) SE Bare 3.74c 0.09 803.57b 45.95 405.49b 38.42 0.16b 0.03 8.06b 0.32 11.69b 0.09 17.32a 1.40 Compost 4.47a 0.09 879.82a 45.95 721.32a 38.33 0.19a 0.03 9.17a 0.32 11.61b 0.09 5.73b 1.40 Woodchips 3.91b 0.09 818.65b 45.97 429.82b 38.33 0.17ab 0.03 8.28b 0.32 12.11a 0.09 12.38a 1.40 ANOVA F Pâ¡ F P F Pâ¡ F Pâ¡ F Pâ¡ F Pâ¡ F P Site (df = 2) 106.84 <0.001 12.29 <0.001 9.52 0.001 20.86 <0.001 45.55 <0.001 9.87 0.001 104.71 <0.001 Mulch (df = 2) 37.53 <0.001 13.12 <0.001 148.42 <0.001 3.81 0.034 0.86 <0.001 25.74 <0.001 106.21 <0.001 Site*Mulch (df=4) 2.76 0.030 1.99 0.098 13.52 <0.001 8.68 <0.001 0.33 <0.001 6.45 <0.001 10.67 <0.001 Responses of these soil nutrients to organic materials were analyzed for two years (2016-2017) at three orchards. Data expressed as mean of n=12 plots per treatment per site and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. Site - the three sites (Sites 1, 2 and 3). â¡ = data were log- transformed for analysis. 56 Table 3.4 Effects of postharvest deficit irrigation on different forms of carbon [C (total carbon), POXC, OrgC (organic C), InoC (inorganic C)] and on OM, C:N ratio and Mn at Sites 2 and 3. Irrigation (Irrig) C (%) SE POXC (mg kg-1) SE OrgC (mg kg-1) SE InoC (%) SE OM (%) SE C:N SE Mn (mg kg-1) SE PDI 3.46 0.09 803.72 39.87 490.47 35.88 0.17 0.03 7.49 0.35 12.06 0.11 4.56 0.21 Full 3.35 0.09 789.65 39.87 463.00 36.03 0.16 0.03 7.38 0.35 12.04 0.11 4.30 0.21 ANOVA F Pâ¡ F P F Pâ¡ F Pâ¡ F Pâ¡ F Pâ¡ F P Site (df=1) 68.15 <0.001 11.10 0.007 1.35 0.270 13.89 0.003 98.13 <0.001 0.69 0.425 5.21 0.043 Irrig (df=1) 2.14 0.146 0.77 0.381 1.96 0.165 1.75 0.188 1.08 0.301 0.10 0.747 0.89 0.348 Site*Irrig (df=1) 1.13 0.289 2.15 0.145 2.19 0.142 0.06 0.814 1.95 0.165 1.32 0.253 1.22 0.272 Mulch (df=2) 20.37 <0.001 6.45 0.002 81.29 <0.001 8.65 <0.001 14.45 <0.001 16.05 <0.001 72.00 <0.001 Site*Mulch (df=2) 5.80 0.004 1.83 0.165 20.83 <0.001 9.44 <0.001 9.83 <0.001 10.68 <0.001 9.05 <0.001 Irrig*Mulch (df=2) 0.32 0.729 0.52 0.595 0.13 0.879 0.06 0.942 0.32 0.730 1.64 0.199 0.22 0.804 Site*Irrig*Mulch(df=2) 1.16 0.316 0.82 0.445 1.53 0.221 0.36 0.700 2.29 0.106 0.50 0.610 2.02 0.137 The effects of organic materials and PDI were examined in 2016 and 2017 at Sites 2 and 3. Data expressed as mean of n=6 plots per treatment per site and standard errors (SE). Site - the two sites (2 and 3). ANOVA shows the P values of Irrigation and its interaction with mulches and site. â¡ = data were log- transformed for analysis. 57 Table 3.5 Responses of soil nutrients to mulch treatments over three sites and two years. Mulch treatment N (%) SE NO3 (%) SE NH4 (%) SE P (mg kg-1) SE K (mg kg-1) SE Ca (mg kg-1) SE Mg (mg kg-1) SE Bare 0.32b 0.01 44.02a 3.81 3.92a 0.55 133.23b 4.87 363.46b 15.84 3835.18b 60.52 309.11b 13.51 Compost 0.39a 0.01 36.39a 3.79 1.45a 0.55 165.03a 4.92 1202.91a 15.84 4123.41a 60.26 461.42a 13.61 Woodchips 0.32b 0.01 14.08b 3.79 1.08b 0.55 123.81b 4.90 375.48b 15.84 3840.61b 60.26 298.96b 13.56 ANOVA F Pâ¡ F Pâ¡ F Pâ¡ F Pâ¡ F Pâ¡ F Pâ¡ F Pâ¡ Site (df = 2) 114.51 <0.001 31.73 <0.001 19.62 <0.001 87.40 <0.001 66.90 <0.001 186.06 <0.001 13.69 <0.001 Mulch (df = 2) 52.23 <0.001 111.24 <0.001 11.32 <0.001 34.42 <0.001 1005.81 <0.001 16.73 <0.001 122.73 <0.001 Site*Mulch(df=4) 2.88 0.024 5.14 0.001 7.39 <0.001 8.19 <0.001 8.46 <0.001 5.71 <0.001 4.70 0.001 Data expressed as mean of n=12 plots per treatment per site and standard errors (SE). Mulch sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. Site - the three sites (1, 2 and 3). â¡ = data were log-transformed for analysis. 58 Table 3.6 Responses of soil nutrients to postharvest deficit irrigation over two sites and two years. Irrigation (Irrig) N (%) SE NO3 (%) SE NH4 (%) SE P (mg kg-1) SE K (mg kg-1) SE Ca (mg kg-1) SE Mg (mg kg-1) SE PDI 0.28 0.09 20.64 2.47 0.90 0.12 153.76 5.79 609.66 16.76 3701.71 68.14 331.24 12.76 Full 0.27 0.09 19.97 2.48 0.93 0.12 146.61 5.82 593.22 16.76 3692.95 68.62 328.23 12.76 ANOVA F P F P F P F P F P F P F P Site (df=1) 61.88 <0.001 43.68 <0.001 5.11 0.045 146.71 <0.001 162.31 <0.001 242.57 <0.001 12.26 0.005 Irrig (df=1) 2.56 0.112 0.00 0.984 0.11 0.738 1.53 0.218 0.85 0.359 0.08 0.782 0.00 0.995 Site*Irrig (df=1) 2.31 0.131 0.04 0.841 2.16 0.144 0.29 0.594 0.90 0.345 0.52 0.472 0.13 0.716 Mulch (df=2) 34.98 <0.001 86.16 <0.001 10.72 <0.001 16.79 <0.001 806.13 <0.001 8.29 <0.001 58.58 <0.001 Site*Mulch (df=2) 8.22 <0.001 3.50 0.034 3.16 0.030 7.46 0.001 4.37 0.015 10.57 <0.001 2.87 0.061 Irrig*Mulch (df=2) 0.35 0.706 2.56 0.082 1.69 0.189 0.09 0.912 0.71 0.496 0.84 0.434 0.46 0.632 Site*Irrig*Mulch(df=2) 1.26 0.287 0.05 0.949 0.70 0.496 1.87 0.159 0.06 0.943 2.76 0.068 0.14 0.870 Data expressed as mean of n=6 per treatment per site and standard errors (SE).Site - the two sites (2 and 3). ANOVA shows the P values of Irrigation and its interaction with mulches and site.59 Table 3.7 Impact of mulch and postharvest deficit irrigation on extractable Al and Na contents, cation exchange capacity, pH and electrical conductivity in soil. Mulch treatment Al (mg kg-1) SE Na (mg kg-1) SE CEC (meq 100 g-1) SE pH SE EC (uS cm-1) SE Bare 1.27a 0.13 30.46b 1.08 22.74b 0.40 6.24b 0.06 31.97b 2.276 Compost 0.64b 0.13 47.80a 1.09 27.74a 0.40 6.86a 0.06 41.16a 2.276 Woodchips 1.25a 0.13 26.58c 1.09 22.77b 0.40 6.42ab 0.06 21.31c 2.276 ANOVA F P F P F Pâ¡ F Pâ¡ F Pâ¡ Site (df = 2) 20.39 <0.001 136.20 <0.001 141.48 <0.001 2.74 0.09 64.9 <0.001 Mulch (df = 2) 7.82 0.001 238.65 <0.001 91.62 <0.001 3.56 0.03 56.99 <0.001 Site*Mulch (df=4) 4.78 0.001 16.65 <0.001 8.93 <0.001 0.86 0.49 3.88 0.005 Sites 2 &3 (2016-2017) Irrigation (Irrig) PDI 0.70a 0.07 26.47 0.73 22.95 0.49 6.82 0.04 22.46 1.31 Full 0.59b 0.07 26.57 0.73 23.08 0.49 6.85 0.04 23.13 1.31 ANOVA F P F P F P F P F P Site (df=1) 0.26 0.622 359.44 <0.001 157.09 <0.001 7.57 0.019 72.50 <0.001 Irrig (df=1) 2.80 0.097 0.01 0.917 0.00 0.967 0.47 0.494 0.00 0.944 Site*Irrig (df=1) 0.07 0.797 1.08 0.302 0.19 0.668 0.14 0.705 0.83 0.364 Mulch (df=2) 0.73 0.483 132.22 <0.001 65.74 <0.001 57.29 <0.001 56.58 <0.001 Site*Mulch (df=2) 0.20 0.819 8.91 <0.001 20.97 <0.001 22.79 <0.001 4.21 0.017 Irrig*Mulch (df=2) 2.41 0.094 1.12 0.331 0.87 0.423 0.44 0.644 0.55 0.580 Site*Irrig*mulch (df=2) 0.15 0.857 0.93 0.399 2.77 0.067 0.93 0.399 0.15 0.858 At three sites, responses of these variables to mulch were analyzed for two years (2016 and 2017), while PDI and its interaction with mulch were examined only at Sites 2 and 3. Data expressed as mean of n=12 plots (mulches) and n= 6 plots (Irrig) per treatment per site and standard errors (SE). Mulch treatments sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1significance level), according to Tukeyâs test. Site+ the three sites (1, 2 and 3). â¡ = data were log-transformed for analysis. 3.3 The response of soil biological properties to mulch treatment and postharvest deficit irrigation Over two years across three sites, application of compost and woodchips increased the total bacterial biomass in the soil relative to bare plots, and there was a tendency for an increase in fungal abundance in woodchips plots relative to bare plots (Table 3.8). The percentage of roots colonized by AMF was lower in compost and woodchips amended plots than plots with bare ground. Neither compost nor woodchips influenced the microbial activities, as determined by FDA hydrolysis. The effect of organic materials on the abundance of soil P. penetrans and total soil nematodes depended on the site (Table 3.8). Application of woodchips 60 at Site 2 increased the abundance of soil P. penetrans and total soil nematode populations compared to control plots, whereas compost amendment at Site1 tended to increase both these groups (Tables D1-D3). The responses of soil biological properties were evaluated from cherry tree plots after PDI implementation at Site 1 in 2017 (Table D1) and across Sites 2 and 3 for two years (Table 3.9). At Site 1, none of the biological properties was affected by PDI, except the high number of total nematodes that was found from fully irrigated plots with compost compared to PDI with compost plots (Table I1). When we compared PDI with full irrigation across two sites (Sites 2 and 3) over two years, PDI increased the total soil bacterial and fungal populations. In addition, the effect of irrigation varied between sites; with a higher microbial activity under PDI compared to full irrigation only at Site 3 (Table D3). Similarly, there was a tendency for root P. penetrans population to be higher under PDI at Site 3 and under full irrigation at Site 2 (Tables D2 & D3). 61 Table 3.8 Effects of mulch treatment on soil biological properties at three sites over two years. These biological properties are colonization by arbuscular mycorrhizal fungi (AMF), abundance of Pratylenchus penetransin roots and soil, total nematodes in soil, microbial activity (fluorescein diacetate - FDA), and abundance of bacteria (16S) and fungi (18S). Mulch treatment AMF (%) SE P.penetrans (1g-1dry root) SE P.penetrans (50g-1 soil) SE Total Nematodes (50g-1 soil) SE FDA (µg g-1) SE Log (16S copy # g-1 dry soil) SE Log (18S copy # g-1dry soil SE Bare 14.19a 1.19 102.83 28.97 20.11 3.29 153.00 18.04 4.54 0.53 9.06b 0.05 7.50b 0.11 Compost 6.04c 1.19 38.03 28.97 20.79 3.29 188.00 18.04 4.64 0.53 9.18a 0.05 7.52ab 0.11 Woodchips 8.52b 1.19 60.32 28.97 26.68 3.29 164.67 18.04 5.13 0.53 9.15a 0.05 7.58a 0.11 ANOVA F Pâ¡ F Pâ¡ F Pâ¡ F Pâ¡ F Pâ¡ F P F P Site (df = 2) 4.25 0.027 0.49 0.619 1.57 0.231 2.43 0.111 54.79 <0.001 19.86 <0.001 9.84 0.001 Mulch (df = 2) 18.84 <0.001 2.10 0.129 1.33 0.269 2.25 0.109 1.47 0.233 6.44 0.002 2.42 0.092 Site*Mulch (df =4) 3.11 0.017 1.42 0.236 4.03 0.004 3.42 0.010 1.28 0.279 0.24 0.9138 1.48 0.211 Data expressed as mean of n=12 plots per treatment per site and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1significance level), according to Tukeyâs test. â¡ =data were log-transformed for analysis 62 Table 3.9 Effects of postharvest deficit irrigation on soil biological properties at two sites over 2016 and 2017. These biological properties are colonization by arbuscular mycorrhizal fungi (AMF), abundance of Pratylenchus penetrans in roots and soil, other nematodes in soil, microbial activity (fluorescein diacetate - FDA), and abundance of bacteria (16S) and fungi (18S). Irrigation (Irrig) AMF (%) SE P. penetrans (1g-1dry root) SE P. penetrans (50g-1 soil) SE Total Nematodes (50g-1 soil) SE FDA (µg g-1) SE Log (16S copy # g-1 dry soil) SE Log (18S copy # g-1 dry soil) SE PDI 9.95 1.16 73.97 32.69 17.83 4.53 162.67 21.52 4.36 0.65 9.01a 0.08 7.36a 0.17 Full 10.44 1.15 64.38 32.69 25.08 4.53 191.00 21.52 3.44 0.66 8.94b 0.08 7.25b 0.17 ANOVA F P F P F P F P F Pâ¡ F P F P Site(df=1) 9.19 0.011 0.06 0.813 0.42 0.531 2.94 0.114 48.84 <0.001 1.90 0.195 0.63 0.445 Irrig(df=1) 1.17 0.282 0.69 0.408 2.03 0.157 1.76 0.188 1.19 0.278 4.43 0.038 7.15 0.009 Site*Irrig (df=1) 1.85 0.17 7.35 0.008 0.90 0.344 0.50 0.483 11.77 <0.001 3.09 0.082 2.36 0.127 Mulch (df=2) 17.09 <0.001 4.96 0.009 4.29 0.016 0.29 0.749 0.52 0.594 5.16 0.007 0.77 0.464 Site*Mulch (df=2) 5.12 0.008 3.14 0.047 4.74 0.011 5.66 0.005 0.95 0.389 0.25 0.777 1.04 0.355 Irrig*Mulch (df=2) 1.52 0.224 0.07 0.932 0.68 0.510 0.07 0.932 1.10 0.336 0.51 0.605 1.33 0.268 Site*Irrig*Mulch (df=2) 3.34 0.039 0.31 0.733 0.23 0.798 1.60 0.207 0.09 0.915 3.05 0.051 2.48 0.088 Responses of biological properties to PDI were assessed at Sites 2 and 3. Results are a mean of n= 6 plots per treatment per site. ANOVA shows the P values of Irrigation and its interaction with mulch and site. Irrigation treatments with different letters within a column differ significantly (at P â¤ 0.05 or P â¤ 0.1significance level), according to Tukeyâs test. â¡ =data were log-transformed for analysis 63 3.4 Responses of sweet cherry tree growth and foliar nutrients to mulch and postharvest deficit irrigation Across three sites and two growing seasons, the organic materials had impacts on trunk cross-sectional area (TCSA), leaf area (LA) and foliar nutrient contents (Tables 3.10 â 3.12). Relative to bare soil, trees had larger TCSAs when grown with woodchips, and had a tendency for greater LA when grown with compost (Table 3.10). Cherry trees grown on plots-amended with compost had higher foliar K and P and lower Mg, Ca and Al concentrations than those grown in non-amended plots (Tables 3.11 & 3.12). Woodchips increased both foliar P and Mn concentrations, but reduced N, S and Cu concentrations compared to those from bare-ground plots. While these overall patterns were observed, the effects of organic materials varied slightly by site (Tables E1 - E7). Application of PDI was first initiated at Sites 2 and 3 in 2016 and at Site 1 in 2017. The effect of PDI on tree growth (TCSA) was evaluated for two growing seasons over the former two sites and in 2017 at the latter site. The results revealed that PDI did not affect TCSA at all sites examined, except a tendency to be higher under co-application of PDI and compost relative to full irrigation with compost across Sites 2 and 3 (Table I1). Additionally, because sample leaves were collected a few days after fruit harvest in the two seasons, PDI was not considered during analysis for leaf area (LA) and foliar nutrients in 2016 at any of the sites. Instead, we examined the effects of PDI on LA and foliar nutrients only in 2017 at Sites 2 and 3.The results showed that neither LA nor any of foliar nutrients were affected by application of PDI at each site (Tables E1 - E7). 64 Table 3.10 The effect of mulch treatment on tree trunk cross-sectional area (TCSA) and leaf area (LA) across three sites over two seasons and postharvest deficit irrigation over two sites and growing seasons. Mulch treatment TCSA (cm2) SE LA (cm2) SE Bare 54.53b 2.90 1679.13b 26.00 Compost 55.60ab 2.91 1742.84a 26.08 Woodchips 57.03a 2.90 1710.79ab 26.08 ANOVA F P F P Site (df=2) 110.58 <0.001 35.040 <0.001 Mulch (df=2) 4.32 0.015 2.720 0.069 Site*Mulch (df=4) 2.09 0.085 0.840 0.505 Irrigation Site 2 and 3 (2016-2017) PDI 66.49 2.97 Full 64.32 2.97 ANOVA F P Site (df=1) 33.95 <0.001 Irrig (df=1) 1.90 0.182 Site*Irrig (df=1) 0.91 0.350 Mulch (df=2) 1.26 0.290 Site*Mulch (df=2) 3.18 0.046 Irrig*Mulch (df=2) 2.76 0.068 Site*Irrig*Mulch (df=2) 0.85 0.433 Data expressed as mean of n=12 (mulch) and n=6 plots (Irrig) per treatment per site per year and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. Response of LA to PDI over two years were not evaluated, because LA was measured at the time of harvest prior to PDI implementation (at Site 1 in 2017 and at Sites 2 & 3 in 2016). 65 Table 3.11 Effects of mulch on the concentrations of key plant leaf macronutrients over two growing seasons (2016 and 2017) at three sites. The macronutrients include nitrogen (N), phosphorus (P), potassium (K), sulfur (S), calcium (Ca) and magnesium (Mg). Mulch treatment N (%) SE P (mg kg-1) SE K (mg kg-1) SE S (mg kg-1) SE Ca (mg kg-1) SE Mg (mg kg-1)SE Bare 2.93a 0.03 0.23b 0.01 2.34b 0.06 0.18a 0.00 1.48a 0.03 0.34a 0.01 Compost 2.98a 0.03 0.24a 0.01 2.69a 0.06 0.18a 0.00 1.37b 0.03 0.30b 0.01 Woodchips 2.86b 0.03 0.24a 0.01 2.40b 0.06 0.17b 0.00 1.50a 0.03 0.34a 0.01 ANOVA F P F Pâ¡ F P F P F P F P Site (df=2) 5.83 0.009 11.09 0.001 23.28 <0.001 22.51 <0.001 46.62 <0.001 2.70 0.089 Mulch (df=2) 9.18 <0.001 10.27 <0.001 79.24 <0.001 5.01 0.008 12.60 <0.001 48.70 <0.001 Site*Mulch (df=4) 1.56 0.186 4.16 0.003 12.58 <0.001 2.38 0.054 0.41 0.799 4.33 0.002 Data expressed as mean of n=12 plots per treatment per site and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. â¡ = data were log transformed for analysis. Table 3.12 Impact of mulches on the contents of key plant leaf micronutrients over two growing seasons (2016-2017) at three orchard sites. These micronutrients include boron (B), copper (Cu), iron (Fe) and manganese (Mn)], aluminum (Al). Mulch treatment B (mg kg-1) SE Cu(mg kg-1) SE Fe (mg kg-1) SE Zn (mg kg-1) SE Mn (mg kg-1) SE Al (mg kg-1) SE Bare 66.78 2.71 6.88a 0.17 145.99 7.09 32.86 3.32 73.89b 1.92 97.72a 6.80 Compost 65.96 2.71 6.72a 0.17 139.72 7.09 33.59 3.32 73.92b 1.89 89.42b 6.80 Woodchips 66.33 2.71 6.26b 0.17 144.71 7.09 34.54 3.32 86.21a 1.88 94.41ab 6.80 ANOVA F P F P F P F Pâ¡ F P* F Pâ¡ Site (df=2) 14.31 <0.001 0.45 0.644 13.30 <0.001 48.47 <0.001 205.13 <0.001 10.14 0.001 Mulch (df=2) 0.29 0.750 7.50 0.001 0.86 0.426 0.77 0.463 14.19 <0.001 3.77 0.025 Site*Mulch (df=4) 1.12 0.347 2.42 0.051 0.26 0.904 1.73 0.146 3.27 0.013 0.61 0.654 Data showed means of n=12 plots per treatment per site and standard error (SE) for each parameter. Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 and P â¤ 0.1 significance level), according to Tukeyâs test. â¡ = data were log transformed for analysis 66 3.5 Impact of mulches and postharvest deficit irrigation on fruit yield and quality Sweet cherries were first harvested at Sites 2 and 3 in 2016 and at Site 1 in 2017. Therefore, to assess the effects of organic materials alone on fruit yield and quality, compost and woodchips were evaluated for plots receiving full irrigation across Sites 2 and 3 for both years and all three sites in 2017 (Table 3.13 & 3.14). In addition, the effect of previous-year PDI on fruit quality was evaluated at Sites 2 and 3 in 2017 only. Over the three sites in 2017 (Table 3.13), compost increased fruit pH, stem pull force and color relative to bare plots. Woodchips also increased fruit color, but reduced fruit firmness compared to bare soil. Similarly, over two seasons at two sites (2 and 3) (Table 3.14), the addition of compost increased fruit TA and pH and decreased SSC, whereas trees amended with woodchips had higher TA and lower fruit firmness relative to the controls. Additionally, there were effects of site and mulch interactions on fruit firmness, TA and color. However, when we examined the effects of mulches on fruit quality attributes at each site, we found no impacts, with very few exceptions (Appendix F). Yield was not affected by the mulch treatments. Application of PDI occurred only after harvest in 2016, so its effect on fruit quality and yield could only be examined in 2017, and only at two Sites 2 and 3 (Table 3.14). Only fruit pH, which was higher in fruits harvested from trees treated with PDI, was affected by irrigation across sites. Also, there was a tendency for higher TA under full irrigation at Site 3. 67 Table 3.13 The effects of mulch on fruit yield and quality of sweet cherry at three sites in 2017. The quality attributes include firmness, total soluble solids (SSC), pH, titratable acidity (TA), fruit color, stem pull force (SPF) and fruit yield. Mulch treatment Firmness (g/mm) SE SSC (%) SE pH SE TA SE Color SE SPF (kg) SE Yield (kg plot-1) SE Bare 444.71a 15.76 21.64 0.22 4.26b 0.01 5.67 0.10 3.66b 0.10 0.78b 0.03 15.76 1.07 Compost 449.45a 15.74 21.58 0.22 4.30a 0.01 5.70 0.10 3.78a 0.10 0.87a 0.03 16.12 1.08 Woodchips 433.05b 15.79 21.84 0.22 4.27b 0.01 5.70 0.10 3.79a 0.10 0.79b 0.03 15.95 1.07 ANOVA F P F Pâ¡ F P F P F P F P F P Site (df=2) 3.79 0.059 2.18 0.164 14.53 0.001 5 0.031 2.29 0.151 0.79 0.481 58.83 <0.001 mulch (df=2) 5.88 0.004 0.42 0.661 5.87 0.004 0.12 0.883 3.45 0.036 6.89 0.002 0.05 0.953 Site*mulch (df=4) 3.11 0.020 1.84 0.129 1.25 0.295 2.68 0.037 4.75 0.002 1.87 0.124 0.48 0.752 Data expressed as a mean of n=6 plots per treatment per site and standard error (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. Fruit harvesting was started at Sites 2 and 3 in 2016, and in 2017 at Site 1. â¡= data were log-transformed for analysis 68 Table 3.14 The effects of mulch and postharvest deficit irrigation on fruit yield and quality of sweet cherry at Sites 2 and 3. The quality attributes include firmness, total soluble solids (SSC), pH, titratable acidity (TA), fruit color, stem pull force (SPF) and fruit yield. Mulch treatment (2016-2017) Firmness (g/mm) SE SSC (%) SE pH SE TA SE Color SE SPF (kg) SE Yield (kg plot-1) SE Bare 425.01a 14.10 20.32a 0.53 4.16b 0.02 5.48b 0.17 3.78 0.09 0.80 0.04 15.31 1.45 Compost 425.56a 14.10 19.90b 0.53 4.19a 0.02 5.68a 0.17 3.81 0.09 0.85 0.04 17.89 1.45 Woodchips 408.55b 14.10 20.54a 0.53 4.16b 0.02 5.67a 0.17 3.86 0.09 0.82 0.04 15.65 1.46 ANOVA F Pâ¡ F P F P F P F P F P F Pâ¡ Site (df=2) 20.35 0.002 1.71 0.228 11.15 0.010 9.35 0.016 3.85 0.085 10.88 0.011 69.03 <0.001 Mulch (df=2) 7.59 0.001 4.23 0.017 3.40 0.037 3.33 0.040 0.93 0.397 2.24 0.112 0.51 0.603 Site*Mulch (df=4) 1.85 0.162 0.62 0.538 2.65 0.076 0.36 0.702 1.03 0.360 1.90 0.155 0.44 0.644 Irrigation (Irrig) (2017) PDI 439.20 22.34 21.79 0.28 4.26a 0.02 5.87 0.14 3.85 0.16 0.83 0.03 16.59 1.11 Full 447.52 22.34 21.66 0.28 4.22b 0.02 5.93 0.14 3.88 0.16 0.83 0.03 18.32 1.10 ANOVA F P F P F P F P F P F P F P Site (df=1) 7.94 0.037 10.57 0.023 4.64 0.084 0.01 0.928 0.00 0.954 0.04 0.857 118.18 <0.001 Irrig (df=1) 1.98 0.165 0.26 0.610 4.78 0.034 0.33 0.567 0.25 0.620 0.03 0.856 2.02 0.162 Site*Irrig (df=1) 0.78 0.380 4.85 0.032 0.79 0.377 4.80 0.033 2.95 0.092 0.01 0.927 0.09 0.770 Mulch (df=2) 5.14 0.009 2.90 0.064 3.51 0.038 2.74 0.075 7.86 0.001 2.12 0.131 0.28 0.760 Site*mulch (df=2) 1.53 0.227 0.58 0.566 2.64 0.082 0.53 0.591 7.36 0.002 2.18 0.123 0.42 0.660 Irrig*mulch 1.79 0.178 1.58 0.217 0.09 0.918 0.01 0.995 0.18 0.834 1.79 0.177 0.75 0.480 Site*Irrig*mulch(df=2) 0.49 0.614 0.66 0.519 1.07 0.351 0.16 0.855 0.59 0.560 0.19 0.831 0.58 0.561 Data expressed as a mean of n= 3-6 plots (mulch) and (Irrig) per treatment per site and standard error (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. â¡= data were log-transformed for analysis. 69 3.6 Responses of soil chemical and biological properties at Site 4 The effects of compost amendment, fumigation and history of previously applied woodchips and P-fertigation on different soil chemical and biological properties were assessed at Site 4 across the two growing seasons (Tables 3.15 - 3.17). Overall, there were higher concentrations of total N, NO3, NH4, K, Ca, Mg, total C, organic C, POX C, inorganic C, organic matter, C:N, and CEC, and lower Mn in compost-amended soils than in non-amended soils. Similarly, the concentrations of total N, K, Mg, Mn, total C, organic matter, organic C, POX C, inorganic C, C:N, NH4, Al, CEC and Ca levels were higher in plots with a legacy of woodchips mulch than in the non-amended plots (no mulch/ no P-fertigation), but had lower pH. Previously P-fertigated plots had high P, Mn concentrations and lower pH compared to non-fertigated plots. Fumigated soils had higher Na concentrations and a tendency for higher P, and lower NO3, K and C: N ratio than their non-fumigated counterparts. The combined application of fumigation along with compost and/or legacy effect treatments had impacts on various soil chemical properties (Tables I2-I4). Fumigated plots with P-fertigation and non-fumigated plots with no legacy effect of mulch or no P had lower N concentrations than the other combinations of fumigation and legacy effect treatments (Table I2). Increased Ca and total C concentrations were detected in non-fumigated plots with legacy effect of mulch amendment relative to all other combinations. Application of mulch with/without fumigation resulted in higher organic matter, organic C and CEC compared to other treatment combinations. By contrast, lower concentrations of Al and Na were found from plots treated with mulch without fumigation compared to the corresponding control plots (mulch-amended with fumigation).The legacy effect of mulch in combination with compost amendment without fumigation also resulted in a higher soil Mg concentration, while a higher soil Mn was recorded in fumigated soil without compost application (Table I4). 70 Biological properties were not affected by the treatments over two growing seasons (Table 3.18), with a few exceptions. Fumigated soil had higher total bacterial populations, whereas a decrease in root P. penetrans populations was detected in plots with compost incorporation compared to the control plots.The plots with the history of P-fertigation had greater total nematodes when comparing with the control plots. There were also a few interaction effects observed at this site. Fumigation with legacy of mulch had a tendency to increase total fungal (18S copies) population compared to non-fumigated and amended plots. Similarly, non-fumigated soils with no compost but with a mulch legacy had higher fungal abundance compared to fumigated soil amended with compost and legacy effect of mulch (Table I5). Additionally, the responses of soil biological properties to compost, fumigation and legacy effects of mulch and P-fertigation were examined for each year separately, and a more-or-less consistent pattern was observed as that seen across the two years together (Appendix H). 71 Table 3.15 Impact of fumigation, compost amendment and legacy effects of mulch and P-fertigation on key soil nutrients in 2016 and 2017 at Site 4. Treatment N (%) SE NO3 (%) SE NH4 (%) SE P (mg kg-1) SE K (mg kg-1) SE Ca (mg kg-1) SE Mg (mg kg-1) SE Mn (mg kg-1) SE Fumigation (n=6): Fum 0.08 0.005 7.55b 2.85 0.99 0.129 69.30a 5.425 185.15b 13.63 1458.38 74.34 166.85 8.84 4.26 0.26 No Fum 0.08 0.005 12.32a 2.79 1.06 0.129 62.50b 5.425 205.86a 13.63 1539.39 74.34 181.10 8.84 3.98 0.26 Compost (n=12): Compost 0.09a 0.005 10.33a 2.82 1.15a 0.125 65.95 4.955 211.78a 13.63 1569.19a 72.04 189.63a 7.48 3.86b 0.26 No Compost 0.07b 0.005 9.54b 2.82 0.89b 0.126 65.85 4.955 179.24b 13.63 1428.58b 72.04 158.33b 7.48 4.38a 0.26 History (n=24): Mulch 0.09a 0.005 9.87 2.87 1.22a 0.122 57.38b 5.039 209.93a 12.76 1564.22a 69.11 180.13a 7.36 5.50a 0.26 No mulch/P$ 0.08b 0.005 8.90 2.83 0.95b 0.122 57.79b 5.039 184.03b 12.76 1446.04b 69.11 169.14b 7.36 3.15c 0.26 P-fert 0.08b 0.005 11.03 2.81 0.90b 0.121 82.53a 5.039 192.56b 12.76 1486.38b 69.11 172.66b 7.36 3.71b 0.26 ANOVA F P F Pâ¡ F P F Pâ¡ F Pâ¡ F Pâ¡ F P F Pâ¡ fum (df=1) 0.78 0.397 11.36 0.006 0.28 0.609 3.53 0.087 3.98 0.071 1.83 0.202 1.77 0.210 1.32 0.275 comp (df=1) 21.68 <0.001 3.58 0.071 4.55 0.044 0.04 0.837 10.24 0.004 9.07 0.006 37.73 <0.001 6.17 0.021 fum*comp (df=1) 0.00 0.974 5.31 0.031 0.33 0.570 7.01 0.015 2.42 0.134 0.02 0.885 4.03 0.057 12.89 0.002 his (df=2) 10.14 <0.001 0.10 0.902 6.79 0.002 45.55 <0.001 7.90 <0.001 12.73 <0.001 4.40 0.015 93.42 <0.001 fum*his (df=2) 10.09 <0.001 3.60 0.032 0.61 0.547 8.53 <0.001 6.06 0.003 10.07 <0.001 1.60 0.208 0.25 0.778 comp*his (df=2) 1.95 0.148 6.37 0.003 0.20 0.818 0.99 0.374 3.19 0.046 0.67 0.513 0.09 0.912 1.25 0.292 fum*comp*his (df=2) 2.19 0.118 9.13 <0.001 1.02 0.364 9.43 <0.001 1.66 0.196 0.63 0.537 2.67 0.074 2.75 0.069 Data expressed as mean and standard errors (SE).History treatments sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. $ no mulch and no P-fertigation. â¡= data were log transformed for analysis 72 Table 3.16 Effects of fumigation, compost amendment and legacy effects of mulch and P-fertigation on different forms of carbon, such as C, POXC, OrgC (organic C), InoC (inorganic C) and on OM and C:N ratio for two seasons (2016 and 2017) at Site 4. Treatment C (%) SE Org C (mg kg-1) SE POXC (mg kg-1) SE InoC (%) SE OM (%) SE C:N SE Fumigation (n=6): Fum 1.08 0.054 194.26 10.248 403.04 14.32 0.079 0.010 2.82 0.175 13.76b 0.237 No Fum 1.15 0.054 195.48 10.248 429.8 14.39 0.082 0.010 3.03 0.176 14.09a 0.237 Compost (n=12): Compost 1.27a 0.054 225.94a 10.248 461.39a 14.36 0.085a 0.010 3.13a 0.176 14.27a 0.237 No Compost 0.96b 0.054 163.8b 10.248 371.46b 14.36 0.076b 0.010 2.71b 0.175 13.578b 0.237 History (n=24): Mulch 1.29a 0.053 219.87a 9.764 455.98a 14.49 0.086a 0.010 3.23a 0.168 15.39a 0.237 No mulch/P$ 1.01b 0.053 181.03b 9.764 387.96b 14.31 0.078b 0.010 2.72b 0.168 13.23b 0.237 P-fert 1.05b 0.053 183.72b 9.764 405.33b 14.23 0.077b 0.010 2.82b 0.168 13.16b 0.237 ANOVA F P F Pâ¡ F P F Pâ¡ F P F P fum (df=1) 2.82 0.121 0.12 0.7325 2.99 0.1117 1.04 0.3296 2.74 0.1258 3.77 0.0783 comp (df=1) 60.71 <0.001 50.49 <0.001 33.78 <0.001 8.63 0.008 11.21 0.003 16.34 <0.001 fum*comp (df=1) 0.07 0.7967 0.02 0.8917 0 0.9597 0.64 0.4312 0.00 0.9804 0.04 0.8488 his (df=2) 58.41 <0.001 24.38 <0.001 13.67 <0.001 15.62 <0.001 35.91 <0.001 141.84 <0.001 fum*his (df=2) 9.67 <0.001 8.08 <0.001 0.88 0.4182 1.13 0.3264 6.59 0.002 4.98 0.009 comp*his (df=2) 1.53 0.223 2.10 0.1291 1.81 0.1693 0.62 0.5409 0.50 0.6079 3.84 0.025 fum*comp*his (df=2) 4.37 0.016 0.39 0.6801 3.37 0.039 0.00 0.9967 0.48 0.6219 9.98 <0.001 Data expressed as mean and standard errors (SE).History treatments sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. $ no mulch and no P-fertigation. â¡= data were log-transformed for analysis. 73 Table 3.17 Effects of fumigation, compost amendment and history effects of mulch and P-fertigation on contents of extractable Al and Na, cation exchange capacity, pH and electrical conductivity for two seasons (2016 and 2017) at Site 4. Treatment Al (mg kg-1) SE Na (mg kg-1) SE CEC (meq 100g-1) SE pH SE EC (uS cm-1) SE Fumigation (n=6): Fum 0.59 0.02 42.19 4.18 9.30 0.46 7.11 0.07 18.01 1.72 No Fum 0.59 0.02 32.31 4.18 9.84 0.46 7.17 0.07 15.89 1.71 Compost (n=12): Compost 0.60 0.02 36.36 4.14 10.09a 0.44 7.15 0.07 17.50 1.71 No Compost 0.58 0.02 38.14 4.14 9.05b 0.44 7.13 0.07 16.40 1.73 History (n=24): Mulch 0.62a 0.02 37.80 4.03 9.99a 0.42 7.03c 0.07 17.59 1.69 No mulch/P-fert$ 0.57b 0.02 35.80 4.03 9.23b 0.42 7.23a 0.07 16.90 1.69 P-fert 0.57b 0.02 38.15 4.03 9.49b 0.42 7.16b 0.07 16.35 1.70 ANOVA F P F Pâ¡ F Pâ¡ F P F Pâ¡ fum (df=1) 0.00 0.9597 9.29 0.011 1.81 0.2056 2.42 0.1481 0.87 0.3716 comp (df=1) 1.31 0.2641 0.06 0.8137 12.69 0.002 0.29 0.5973 0.64 0.4312 fum*comp (df=1) 0.19 0.6694 0.45 0.5111 <0.001 0.994 1.05 0.3156 0.56 0.4638 his (df=2) 3.57 0.033 2.01 0.1407 15.75 <0.001 17.9 <0.001 0.17 0.8455 fum*his (df=2) 3.94 0.023 3.45 0.036 10.52 <0.001 0.07 0.9284 2.65 0.0765 comp*his (df=2) 0.52 0.5954 0.01 0.9887 0.55 0.5763 1.72 0.1859 0.81 0.447 fum*comp*his (df=2) 3.20 0.046 1.09 0.3423 0.35 0.709 1.47 0.2347 3.27 0.043 Data expressed as mean and standard errors (SE).Historical treatments sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. $ no mulch and no P-fertigation. â¡= data were log-transformed for analysis. 74 Table 3.18 Effects of fumigation, compost and history effects of mulch and P-fertigation on soil biological properties over two seasons at Site 4. Treatment AMF (%) SE P. penetrans (1g-1dry root) SE P. penetrans (100g-1 soil) SE Total nematodes (100g-1 soil) SE FDA (µg g-1) SE Log (16S copy # g-1 dry soil) SE Log (18S copy # g-1 dry soil) SE Fumigation (n=6) Fum 25.93 2.15 165.92 55.20 94.70 18.25 571.17 71.97 2.55 0.35 8.72a 0.05 7.00 0.14 NoFum 22.63 2.15 271.74 55.20 71.39 18.21 616.00 71.97 2.17 0.35 8.62b 0.05 7.09 0.14 Compost (n=12) Compost 23.01 2.06 132.14b 46.31 78.65 18.25 554.83 71.69 2.39 0.29 8.68 0.05 7.01 0.14 NoComp 25.54 2.06 305.51a 46.31 87.44 18.21 632.33 71.69 2.33 0.29 8.65 0.05 7.08 0.14 History (n=24) Mulch 24.90 2.19 204.31 51.01 73.86 16.81 574.25ab 69.56 2.49 0.27 8.74a 0.05 7.07 0.14 Nomulch/P-fer$ 24.23 2.19 219.36 51.01 84.39 16.91 560.25b 69.56 2.30 0.27 8.61b 0.05 7.00 0.14 P-fert 23.70 2.19 232.82 51.01 90.89 16.81 646.25a 69.56 2.28 0.27 8.66b 0.05 7.06 0.14 ANOVA F P F P F P F Pâ¡ F P F P F P fum (df=1) 1.20 0.297 2.08 0.177 0.66 0.434 1.49 0.248 0.57 0.467 5.32 0.042 0.94 0.352 comp (df=1) 0.84 0.369 6.95 0.015 1.36 0.257 0.24 0.631 0.04 0.847 0.69 0.415 1.86 0.186 fum*comp(df=1) 1.36 0.255 3.08 0.093 2.69 0.115 1.29 0.268 0.69 0.415 0 0.968 0.38 0.545 his (df=2) 0.10 0.908 0.19 0.831 0.66 0.518 3.83 0.025 0.68 0.510 7.58 <0.001 1.56 0.216 fum*his (df=2) 2.14 0.123 0.87 0.422 1.84 0.165 1.42 0.248 1.62 0.204 0.61 0.546 0.98 0.379 comp*his (df=2) 0.20 0.816 0.54 0.587 0.75 0.476 0.79 0.456 1.05 0.356 1.81 0.171 0.68 0.507 fum*comp*his(df=2) 1.39 0.254 3.34 0.041 0.51 0.600 0.17 0.846 0.04 0.961 0.49 0.616 2.66 0.076 Data expressed as mean and standard errors (SE). Historical treatments sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. $ no mulch and no P-fertigation. â¡= data were log transformed for analysis.75 Chapter 4: Discussion In this chapter, the effect of mulch treatment on all measured variables from soil and cherry trees are discussed, followed by discussions on the effect of PDI on the same variables. 4.1 Effect of mulches on soil moisture content and chemical properties The Okanagan Valley has diverse climatic conditions, with a wide range of temperature and precipitation across different locations and growing seasons. Our project was established at sweet cherry orchards located at three main sites in the Valley: in Coldstream (Site 1), Lavington (Site 2) and East Kelowna (Site 3) for two years, 2016 and 2017. According to the records from local weather stations of Environment and Climate Change Canada, the second growing season (2017) was drier across all sites examined relative to 2016. Soil moisture plays critical roles in determining soil chemical and biological properties, particularly, the rate of different soil processes to release nutrients, the accumulation or removal of organic and inorganic compounds, and the growth and development of microbes (Drenovsky et al. 2004; Barnett and Johnson 2013; Yan et al. 2015; Dangi et al. 2016; Lvova and Nadporozhskaya 2017). In this study, we found greater soil moisture content in compost amended plots than non-amended or woodchips plots. In agreement with our finding, compost mulch maintained the volumetric soil moisture content more effectively than incorporated compost in ornamental shrubs (Gonzalez and Cooperband 2002). A recent study also revealed that incorporated manure compost increased available soil water to grapevines due to high water holding capacity (Ramos 2017). By contrast, the lack of response of volumetric soil moisture to woodchips was unexpected in our study. It has been shown that application of different types of mulch (e.g., straw, plastic, etc.) prevents soil water evaporation and retains soil moisture (Kumar and Dey 2011; Kasirajan and Ngouajio 2012; Kader et al. 2017). The type 76 of mulch material applied (woodchips), which had high porosity, may have led to high evapotranspiration. Soil organic materials play an important role in soil chemical properties in diverse agricultural fields (Dalal et al. 2003; Tejada et al. 2009; Allen et al. 2011; Torres et al. 2015). In line with these reports, we found greater POXC, phosphorus, total nitrogen, total carbon, organic carbon, potassium, organic matter, EC, pH and CEC with compost than in the non-amended soil. For instance, addition of compost, including yard waste, provides several nutrients, such as K, P, N and organic C, and can increase pH and CEC in soils (MiloÅ¡eviÄ et al. 2013). Woodchips also had a positive effect on most of these variables when compared to bare ground; however, some of these effects were slightly less than that of the compost effects. This could be due to the higher nutrient content of our yard-waste compost compared to the wood-chip. Another possible explanation would be because woodchips require a longer time than compost to decompose and release carbon and nutrients to the soil (Gonzalez and Cooperband 2002; Yang et al. 2003). The type of mulch materials (e.g., yard waste vs woodchips) applied to the soil are also other possible factors that could affect the activity of organisms involved in the decomposition process in the soil due to the different composition of nutrients and C:N ratio from these organic sources, and eventually affect the rate of nutrient cycling and amount of nutrients released to the soil (Westphal and Becker 1999; Westphal 2005). 4.2 Soil biological properties as affected by compost and woodchips Surface-applied yard waste compost and woodchips affected some of the soil biotic properties examined after two consecutive years of mulch treatments. These findings were partly in agreement with previous studies describing that an increase in soil organic matter results in an increase in soil microbial abundance, activity and diversity (Bonilla et al. 2012a; Bonilla et al. 2012b; Lucas et al. 2014). 77 There was an increase in total bacteria and a tendency for an increase in total fungi in compost and woodchips soil relative to the non-amended soil. This outcome, especially for compost could be explained by its composition of organic C, which may exist in a simpler form and which could be easily accessible by soil microbes. Similar results have been observed with agricultural waste compost, which increased total bacterial abundance on newly planted sweet cherry in the Okanagan Valley (Watson et al. 2017). However, the effect of woodchips on total bacterial and fungal biomass despite no change in soil organic C was not clear. The DNA abundance of bacteria and fungi was expected to be positively correlated to microbial activities; however we did not observe the expected higher activities in compost-amended soils. This response may be because microbial DNA quantified by qPCR comes from biologically active, inactive and dead cells of microbes, whereas only active microbes are targeted during microbial activity analysis (Caldwell 2005). It is likely that compost introduced inactive/ dead microbial cells into soils and that qPCR amplified DNA from those cells (Strickland and Rousk 2010). Another explanation could be that the high levels of N (increased in compost-amended plots) limited the enzymatic activities of soil microbes (Dick 1992). Unlike other soil biotic properties, there was a lower percentage of AMF root colonization in trees grown with compost and woodchips amendment when compared with those grown in bare ground over two growing seasons at all three sites. These results are in contrast to several other studies showing that organic materials, including compost, promoted AMF root colonization, hyphal growth and sporulation, and subsequently enhanced nutrient uptake (e.g., P) and plant growth in different crops (Cavagnaro 2015; Meyer et al. 2015; Saia et al. 2015; Yang et al. 2018). By contrast, several other studies found a negative effect of organic amendments on colonization by AMF, and cited high levels of added P and N as the cause (Liu et al. 2012; Cavagnaro 2015; Cozzolino et al. 2016). Colonization by AMF tends to be lower in high-nutrient than low-nutrient soils (Liu et al. 2016). We also found higher levels of P and total 78 N in compost-amended soils than in non-amended soils, but neither soil P nor total N was enhanced by the addition of woodchips. Because AMF colonization was reduced with both types of amendment, high contents of P and N in the soil may not be the explanation. Instead, an alternate explanation could be a stimulation of root growth by organic amendments, as was observed when urban organic waste compost was added to maize and hay mulch in strawberry fields (Kumar and Dey 2011; Jindo et al. 2012). AMF colonization is expressed as a percentage of the root length that is colonized; therefore, if total colonized root length remains constant, but root length is higher, the percentage colonized will be lower (Treseder 2013). Although we did not determine root length density in the soil, we noticed during soil sampling that there was greater fine root growth in amended soils than that in non-amended soils. Furthermore, root lengths of cherry seedlings were higher in some of these same soils when amended with compost or woodchips in a greenhouse study (Munro 2018). The levels of some soil nutrients such as total N, total C, and organic C supplied by organic sources (e.g., compost) are associated with lower abundances of P. penetrans (Stirling et al. 2003) and other plant parasitic and disease-causing pathogens (Janvier et al. 2007; Bonilla et al. 2012b). A recent report in the Okanagan Valley showed that compost-amended soil reduced P. penetrans populations compared to that of non-amended soil in sweet cherry trees (Watson et al. 2017). In our study, incorporated compost at Site 4 and compost mulch at Sites 2 and 3 caused a reduction in root P. penetrans. However, the reduction response for compost was not detected over three sites despite increases in the total N, total C, and organic C contents from plots amended with compost mulch. On the contrary, woodchips increased soil P. penetrans and total nematode populations at Site 2. It has been reported that although soil organic materials supply nutrients to soil and plants and eventually improve crop yields, its effect on P. penetrans depends on type of mulch and its by-products, and length of time after application (McSorley and Gallaher 1997; McSorley 2011). A study using yard-waste compost 79 did not show a reduction effect on Pratylenchus spp. in vegetable crops (McSorley and Gallaher 1996). It has been proposed that toxic by-products from the decomposition of mulches or biological antagonists simulated by organic materials may suppress the Pratylenchus populations (McSorley 2011). However, in our study, there would have been slow decomposition of the organic materials such that any toxic by-products might be released too gradually to be effective. 4.3 Effects of mulches on plant water status, foliar nutrients and fruit quality Compost mulch increased soil moisture, but a more important measure for crop production is plant moisture status. In this case, neither compost nor woodchips affected tree stem water potential, a useful indicator of plant water status. Our result agrees with results reported by Nguyen et al. (2013) showing that compost mulch had no effects on stem water potential of grape vines. The limited effect of mulches (or soil moisture) on plant water status may be due to sufficient amount of irrigation water supplied to meet the plant water need across all treated plots. Most fruit trees can exhibit stem water potential values ranging from -0.5 to -1.0 MPa under frequent irrigation (Shackel et al. 1997; McCutchan and Shackel 2019). For example, in cherry trees cultivated in California, stem water potential was correlated with rate of shoot growth, and the shoot growth stalled when stem potential values reached between -1.5 and -1.7 MPa (Shackel et al. 1997). In our study, most stem water potential values were within the range of -0.5 to -1.0 MPa, except during the hottest months, particularly late August in 2016 at Site 3 and late September in 2017 at Site 2, when stem water potential values dropped to within the range of -1.5 to -1.7 MPa. Addition of compost or organic mulch to the soil often stimulates plant growth and crop productivity (Abouziena et al., 2008; Forge et al., 2008, 2003; Neilsen et al., 2003; Sas-Paszt et al., 2014; Stefanelli et al., 2009; Treder et al., 2004).This plant response has been attributed to 80 release of nutrients to the soil, and increased soil microbial biomass. In our case, surface application of compost and woodchips resulted in altered concentrations of a few foliar nutrients. Specifically, trees that were grown in compost treated soil had higher foliar K and P, and lower Mg and Al levels than those grown on bare ground. Leaves of trees grown with woodchips had higher P and Mn concentrations relative to trees with no amendment. High levels of foliar K and P from compost- or woodchips-amended plots were likely due to the increased availability of these nutrients in treated soils. In return, higher K and pH in soils can reduce plant uptake of Mg and Al, respectively, in soils (Yike Bing 2011; Baquy et al. 2018). In agreement with our findings, mulched compost increased foliar nutrients, particularly P concentrations in grape vine (Nguyen et al. 2013). Although organic materials can improve soil nutrient contents, the effect of mulch on these nutrients was inconsistent across sites. For example, foliar total N and S were higher in trees with compost at Site 3, but not at Sites 1 and 2. The reason behind this could be the different rootstocks used at each site (Neilsen and Kappel 1996). Cherry trees with woodchips treatment had greater tree growth (as measured in TCSA) than control trees, but no difference when compared with trees with compost treatment. This is probably due to the promotion of finer root growth by woodchips amendment, which can lead to enhanced nutrient and water uptake by the plant (Volder et al. 2005). By contrast, compost showed a tendency for an increase in leaf area relative to controls. It has been shown that the amount and type of mulch applied to the soil can affect nutrient cycling and the amount of nutrients released to the soil by influencing the activity of organisms involved in the decomposition process and, thereby, plant growth rate (Westphal and Becker 1999; Westphal 2005). For example, use of mulch (alfalfa) and biosolid for seven years enhanced tree growth and yield of apple in the southern Okanagan (Neilsen et al., 2003). Similarly, in Colombia, grass residue mulch in a field trial for eight years increased root and top biomass of cassava (Cadavid et al. 1998). 81 Although fruit yield was not affected by mulch treatment, some aspects of fruit quality including pH, stem pull force and color were higher in samples harvested from plots-amended with compost. We also detected an increase in fruit color and decrease in fruit firmness by woodchips. Most of our fruit quality results are in contrast to a recent report from Australia showing that a humified compost (made from high-quality manure) amended for two seasons increased total soluble solid and decreased firmness and stem pull force of sweet cherry without influencing its export quality (Tan et al. 2018). The differences between our results and previously reported findings could be due to variations in cultivated varieties and type of organic materials (e.g., mulch types) used in the different studies. Additionally, in partial agreement with our findings, use of bark mulch improved fruit quality of sweet cherry during the first few seasons (Neilsen et al. 2006). These increased fruit quality aspects from trees grown with compost and woodchips amendments would be due to the presence of higher levels of organic material-derived soil nutrients, particularly P and K, which have impacts on fruit quality (e.g., color and total soluble solids) (Å tampar et al. 2015; Hasanuzzaman et al. 2018). 4.4 Impacts of postharvest deficit irrigation on soil moisture content and biochemical properties Implementation of PDI saves water use by irrigation depending on the levels of deficit, and its effect on plant growth and productivity has been examined in different fruit trees (e.g., sweet cherry, peach and plum) (Ruiz-Sanchez et al. 2010; Behboudian et al. 2011; Chai et al. 2016). However, studies on the effect of PDI on soil water content and soil biochemical properties remain limited. Soil moisture content can be used as a soil water status indicator for regulated deficit irrigation application in sweet cherry and peach (Dangi et al. 2016; Blanco et al. 2018). Supply of an adequate amount of irrigation water maintains favorable soil moisture in the root zone, but PDI can limit the amount of water in the soil depending on the level of water 82 deficit. A lower soil water content can decrease nutrient cycling due to the negative effect on soil microbial communities and activities (Drenovsky et al. 2004). However, in a recent study conducted in California, soil water content and microbial communities were not affected by PDI implemented with different irrigation techniques compared to the corresponding full irrigation for seven years at the peach orchard (Dangi et al. 2016). We observed increases in populations of total bacteria and fungi in soils treated with PDI relative to fully irrigated soils at Sites 2 and 3. The microbial activity was also increased by PDI at Site 3 only. Previous studies showed that moist soil contained diverse microbial populations (Unger et al. 2009). The mechanisms of how deficit irrigation, particularly PDI, positively impacts soil microbes have not yet been described. In fact, moist soils have higher respiration and water fluctuation than dry soils in summer, and excessive soil water can cause a decreasing microbial activity and abundance, likely due to limited oxygen diffusion and creating unfavorable conditions to microbial growth and development (Yan et al. 2015; Borowik and Wyszkowska 2016). However, soil moisture was not affected by implementation of PDI in our study, suggesting that low soil moisture is not the explanation for the effect of PDI on microbes. Instead, soil disturbance during installation of PDI lines and frequent fixing of some soil moisture data loggers and sensors on PDI plots may have facilitated more diffusion of oxygen into soil to create favorable conditions for microbial growth and development. The population of root P. penetrans was higher under PDI at Site 3 and full irrigation at Site 2. Due to such inconsistent results at these sites, it is difficult to conclude that there was an effect of irrigation treatment on root P. penetrans. Instead, the random sampling of roots, the different soil textures at Site 2 (sandy loam) and Site 3 (loamy sand), and different rootstocks at the sites could be the cause for the different response. These results highlighted the interactive uses of PDI and mulch treatment, in which further evaluation would be needed for more seasons at these sites and others. 83 4.5 Effect of postharvest deficit irrigation on plant water stress, growth and fruit yield and quality Stem water potential has been known to be a reliable, stable, accurate and sensitive physiological indicator of the water status in different plants, including prune, plum and cherry trees (Remorini and Massai 2003; Jones 2007; McCutchan and Shackel 2019). Using data from two growing seasons at three sites, the stem water potential values were relatively high (i.e., less negative) in both early June and late September regardless of irrigation treatment. The responses of stem water potential to deficit irrigation can be varied depending on plant species or variety, soil type, tree age and phonological stage (Behboudian et al. 2011; Galindo et al. 2018). Apparently, higher stem water potential was recorded from fully irrigated plots-amended with woodchips mulch relative to bare plots with PDI at Site 2 over two growing seasons. Taken together, use of regulated deficit irrigation is an important orchard management practice to save overall irrigated water and to increase plant water stress tolerance (Ruiz-Sanchez et al. 2010; Behboudian et al. 2011; Chai et al. 2016). Postharvest deficit irrigation with a 25-28 % reduction in applied water had no effect on cherry tree growth (as measured by TCSA), productivity (leaf area) and foliar nutrient concentration, when compared to full irrigation. In some cases, however, PDI has had negative effects on productivity of fruit trees by reducing excessive vegetative vigor, and on flower bud induction and floral differentiation processes, for example in stone fruit trees (Galindo et al. 2018). Vegetative growth correlates to sufficient water supply that can maintain the plant evapo-transpiration (Menzel 2017). As with the stem water potential results, this implies that PDI supplied sufficient water to support growth of cherry trees. In line with our results, a relatively similar vegetative growth was maintained in apricot trees after implementing deficit (25 % reduced) and full irrigation for three consecutive years in Turkey (Bozkurt et al. 2015). By 84 contrast, deficit irrigation by over 50 % water reduction at the same apricot orchard negatively affected tree growth. Trees under high deficit irrigation may reduce leaf growth and fully/ partially close the stomata, which reduces photosynthesis, and eventually decreases leaf area and tree growth (Cohen et al. 2001; Menzel 2017). However, the effects of deficit irrigation on tree growth can be varied depending on tree species and climatic conditions, as PDI (60% reduced) did not affect plum tree growth in Japan (Samperio et al. 2015). In addition, the concentration of foliar nutrients may be associated with the available soil moisture for nutrient solubility in the soil and soil nutrients that can be taken up by plant roots (Jacoby et al. 2017). In agreement with our results, foliar nutrient composition remained similar after implementation of 25-50 % reduced irrigation at different growth stages of a peach tree in Spain (Abrisqueta et al. 2011). Nevertheless, the effects of PDI on cherry trees can be negative when the level of deficit irrigation increases further, depending on species/cultivars, rootstocks, soil type and climatic conditions (Neilsen and Kappel 1996). Fruit yield or quality is not expected to be negatively affected by regulated deficit irrigation as long as the deficit irrigation is implemented after fruit harvest (Behboudian et al. 2011). For example, application of PDI (up to 60% reduction) had no impact on the subsequent yield and short-term fruit quality of sweet cherry in Spain, with a slight reduction in firmness and SSC after cold storage (Marsal et al. 2010). Similarly, Marsal et al. (2010) found that cherry fruit yield and quality were similar between trees treated with deficit and full irrigation in the following year, except fruit pH, which was higher in PDI-treated plots. Furthermore, when deficit irrigation (25-50% reduction) was applied to sweet cherry trees in Turkey, none of these irrigation levels affected TA, SSC, pH and fruit weight of sweet cherry (Demirtas et al. 2008; Correia et al. 2017). Postharvest deficit irrigation is applied after harvest when trees are least sensitive to water stress. Our results support other reports that tree growth and fruit yield or quality are not 85 negatively affected by PDI (Goldhamer and Beede 2004; Behboudian et al. 2011; Chai et al. 2014). 4.6 Effects of fumigation, compost and legacy of P-fertigation and mulch on soil chemical and biological properties at Site 4 We assessed the interactive effects of fumigation, beef feedlot-based compost, and âlegacyâ effects of bark mulch and P-fertigation on soil biological and chemical properties for two seasons at Site 4, an established southern Okanagan orchard. Fumigation and beef feedlot-based compost were applied to plots prior to the replanting of cherry trees. The âlegacyâ mulch plots had surface-applied shredded bark added annually from 2005-2014, and the residual mulch was incorporated into the soil prior to replanting with cherry trees in 2015. Overall, as expected, the plots with incorporated compost or a legacy of mulch had higher organic C, organic matter, total N and other important nutrients, while higher P was detected in legacy P-fertigated plots relative to the corresponding non-treated plots. Incorporating both compost and mulch into the soil, provided soil microbes with increased opportunity to decompose the organic amendments and release nutrients to the soil. Furthermore, the mulch had up to 12 years to break down prior to measurement in this study. This might explain the stronger effect of mulch at Site 4 compared to Sites 1-3. The use of organic materials, such as compost, can suppress P. penetrans while maintaining soil fertility and improving microbial abundance and/ or activity (McSorley 2011; Bonilla et al. 2012b; Forge et al. 2016). Therefore, it was not surprising that application of compost alone or in combination with fumigation at Site 4 suppressed P. penetrans. These results are also consistent with a recent study in the southern Okanagan (Summerland) that found use of compost and bark mulch decreased P. penetrans populations in a sweet cherry orchard (Watson et al. 2017). Application of fumigation alone and legacy of mulch also 86 increased bacterial biomass while fungal populations increased where no fumigation was combined with no compost and a legacy of mulch. Fumigation can have a significant impact on soil organisms, depending on whether the application was for a short or long period of time (Ibekwe et al. 2001; Dangi et al. 2017). In particular, fumigation decreased the population of plant pathogens, including Pratylenchus, and increased beneficial microbial populations, which may have rebounded after initial population decline by using organic carbon from dead cells of microorganisms (Ibekwe et al. 2001). However, pathogen populations can sometimes recover by the end of the first growth season following fumigation. Despite these benefits, fumigation has become a less acceptable management practice in orchards due to regulatory controls prohibiting the use of fumigant chemicals (Forge et al. 2016). Additionally, most of the treatments applied individually or in combination at Site 4 had no effects on microbial activity and AMF root colonization. Unexpectedly, these results did not coincide with soil nutrient and microbial biomass differences observed between amended and non-amended plots. We noticed that some plots had poorly performing experimental trees, with stunted, poor growth and distribution of roots due to unknown reasons. This poor tree health could have contributed directly or indirectly to the unresponsiveness of these variables for the treatments. 87 Chapter 5: General conclusion This study investigated sweet cherry production in the north and central areas of the Okanagan Valley using postharvest deficit irrigation and mulches over two years. The warming trends, increasing frost-free days and a longer growing season in these areas, create favorable conditions for the commercial cultivation of sweet cherries (Neilsen et al. 2017). However, the availability of adequate and regular water supply is a major concern for sustainable production of this economically important crop due to competition for fresh water between crop production and a rapidly growing urban population (Neilsen et al. 2006; Neilsen et al. 2016). Use of a regulated deficit irrigation approach, particularly postharvest deficit irrigation, has been shown to address the issues of limited water supply without negative effects (Ruiz-Sanchez et al. 2010; Behboudian et al. 2011; Chai et al. 2016). Soil biochemical properties are also known to be useful indicators for soil health, and subsequently influence directly and indirectly plant growth and productivity (Treder et al. 2004; Abouziena et al. 2008; Forge et al. 2008; Stefanelli et al. 2009; Sas-Paszt et al. 2014). The status of soil health in the northern areas of the Okanagan Valley remains unknown, as two of these areas in this study are being used for sweet cherry cultivation for the first time. Soil organic materials have been shown to be an important approach for maintaining soil moisture, and supplying soil nutrients and organic matter. In turn, increased organic matter helps retain and encourage beneficial soil microbes, and mitigate future soil-borne plant pathogens in cultivated soils (Forge et al. 2003; Neilsen et al. 2003b; Forge et al. 2008; Sas-Paszt et al. 2014; Forge et al. 2016). Hence, my thesis project was proposed to investigate the effects of postharvest deficit irrigation (PDI) and mulch treatments on soil health status, the stem water potential, productivity and fruit quality of sweet cherry trees of newly and previously-established orchards in the Okanagan Valley over two growing seasons. 88 We assessed postharvest deficit irrigation (PDI) with up to 28% reduction of irrigated water on soil biochemical properties, plant water status, productivity and fruit yield and quality. A few of the measured soil chemical and biological properties were affected by PDI implementation. Nevertheless, there were no effects of PDI observed on any cherry tree growth and productivity parameters, such as mid-day stem water potential, trunk cross-sectional area, leaf area and fruit yield and quality when evaluated over the two growing season at all three sites. The results are in accord to our hypotheses. Although the response of cherry trees to PDI depends on rootstocks and cultivars used, the current results suggest that it may be possible to cultivate sweet cherry with PDI in the north and central areas of the Okanagan Valley. The effects of mulches on soil chemical and biological properties were investigated at four sites over two years. The results showed that orchard plots amended with compost mulch contained higher concentrations of soil nutrients (e.g.,organic matter, N, P, K, etc) and higher levels of pH, EC and CEC than non-amended plots at the three sites. At an established orchard (Site 4), plots with pre-plant incorporation of compost or a legacy effect of mulch had a greater concentration of most soil nutrients and organic matter along with higher pH, EC and CEC, while the content of P was increased from plots with the legacy effect of P fertigation. These findings support our hypotheses. Application of organic materials, for example, yard waste, provides several nutrients, such as K, P, N and organic C, and can enhance pH and CEC in soils (MiloÅ¡eviÄ et al. 2013). In contrast to our hypothesis, woodchips mulch had limited impact on soil chemical properties at the three sites, perhaps due to its low chemical composition and requirement of a longer time to be decomposed and release nutrients to the soil (Gonzalez and Cooperband 2002; Yang et al. 2003). As expected, the two year data revealed that compost and woodchips increased the abundance of total bacteria (as 16S copies), with a tendency for an increase in total fungal population relative to bare ground at three sites. However, neither compost nor woodchips had 89 effects on microbial activity, which is in contrast to our hypothesis. The reason behind this was likely due to surface application of organic materials, soil sampling with a single soil profile fraction (the depth of 0-30 cm) and short length of time after application. Although soil sampling at depth of 0-30 cm is the common practice (Meyer et al. 2015), surface application of organic sources for two seasons can have effects on soil biological properties only in the top few centimeters of soil (Yang et al. 2003; Cogger et al. 2008). Given that, soil sampling at 0-30 cm depth may mix well-amended soils from the top few centimeters with less amended soils to 30 cm depth, and thus influence the effects of the surface-applied organic materials. Similarly, total bacteria were positively affected by fumigation alone and legacy effect of mulch. Fungal populations also were increased by a legacy effect of mulch on plots with no other treatments. We found a lower percentage of AMF root colonization in trees grown with compost or woodchips plots as compared to those grown in bare ground when considered over two years combined at three sites. The use of mulch (e.g., compost) increases the levels of soil nutrients, such as P and N, which, in turn, can negatively affect AMF root colonization (Liu et al. 2012; Cavagnaro 2015; Cozzolino et al. 2016). However, there was no change in percentage of root colonization by AMF at plots that were fumigated and had either compost incorporation or legacy effects of mulch and P-fertigation observed at Site 4. These results partially support our hypothesis. Although woodchips mulch did not suppress root and soil P. penetrans and soil total nematode populations, compost mulch reduced root P. penetrans at Sites 2 and 3, and use of incorporated compost alone or in combination with fumigation suppressed root P. penetrans at Site 4, partially supporting our hypothesis. It has been shown that organic amendments stimulate beneficial microbes in the soil by supplying the required nutrients and that can lead to direct or indirect suppressive effects on plant-parasitic nematodes (Kimpinski et al. 2003; Forge et al. 2008). The effects of organic materials on soil and root nematodes can vary with the 90 nematode species, host crop cultivar, type of amendment and its by-products, length of time after application and experiment type (field or greenhouse) (McSorley and Gallaher 1996; McSorley and Gallaher 1997; McSorley 2011). A slow decomposition rate of the organic materials applied and the short length of time after application in this study may have released too low a concentration of nutrients to be effective. The nutrients can be used as a source of energy for antagonistic microbes and for uptake by the plant. Additionally, the supplied organic materials may not have been enough to detect its effects on nematodes, as large amounts of organic sources are needed for effective control and are not feasible for large areas (McSorley 2011). The effects of mulches were also assessed on soil moisture content, plant water status, plant growth (Trunk cross-sectional area-TCSA), productivity (leaf area), foliar nutrients and fruit yield and quality at three sites. Compost mulch (not woodchips) maintained high volumetric soil moisture, partially supporting our hypothesis. Compost or woodchips increased TCSA, leaf area and some fruit quality attributes. It has been shown that a poor nutrient content or quantity of organic materials applied to the orchards may not provide adequate nutrients/ soil buffer conditions leading to no effects on tree growth and productivity, as nutrient cycling and amount of nutrients released depend on the quality and quantity of organic sources and decomposition process (Westphal and Becker 1999; Westphal 2005). Additionally, there were effects of mulches on a few foliar nutrients (e.g., K, P & S). However, these effects also were affected by site, as the presence of heterogeneity among orchards in soil fertility prior to the application of amendment and the type of cherry rootstocks used (Neilsen and Kappel 1996) may contribute to variations in concentrations of foliar nutrients. Overall, this study showed that the more northern and central areas of the Okanagan Valley can grow sweet cherry for commercial purposes using postharvest deficit irrigation after harvest without any negative effects. The postharvest deficit irrigation approach can save up to 91 28 % of irrigated water per growing season and may reduce excessive vegetative growth. Additionally, compost application through surface application or pre-plant incorporation may be an important approach to increase organic matter, soil nutrients and other chemical properties (i.e., pH, CEC and EC) as demonstrated over two seasons at all orchards examined. Although compost showed a positive effect in suppressing P. penetrans and increasing total bacterial populations, no consistent effects were detected on other soil biological properties across sites. Woodchips may also be another option to increase soil nutrients and organic matter, if it is incorporated into the soil and kept for a long time, as observed at Site 4. However, surface-applied mulch had limited effects on soil biological properties and soil nutrient within three years of placement in orchards (as observed at Sites 1-3). Hence, the use of organic materials may be a useful approach to improve and/ or restore soil organic matter and nutrients, and subsequently, maintain soil health and improve plant growth and productivity as a long-term investment in fruit trees. However, organic materials need to be applied every growing season to be effective, which may be challenging because of higher cost to purchase, transport and apply the organic sources to the orchards. This could minimize its applicability for growers for long-term sweet cherry production. Although this study addressed key research questions on sweet cherry production in these areas of the Okanagan Valley, it had a few limitations. Postharvest deficit irrigation was implemented at three sites during different seasons, for example, in 2016 at Sites 2 and 3 and in 2017 at Site 1. It was challenging to assess the effect of PDI on multiple sites over seasons. The second limitation was the use of different sweet cherry varieties at the experimental orchards, making it difficult to compare fruit yield and quality across sites due to the presence of inherited genetic variations across the varieties. Soil sampling at only 0-30 cm depth in the soil profile was another challenge of this study because soil biological properties can be diverse in different fractions of soils (e.g., 0-5 cm, 5-15 cm and 15-30 cm) after applying organic sources 92 on the surface of the soil. The study was also conducted for only two seasons, which may not be enough time to detect the long-term effects of PDI (e.g., on fruit yield and quality) and surface-applied woodchips mulch that takes more time to decompose and release nutrients to soil than compost. Additionally, a few trees at the experimental plots performed poorly with stunted growth, particularly at Site 4, which may have caused high variation within treatments and between seasons. Taking these limitations into consideration, further research is needed to assess the impact of PDI with greater irrigated water reduction over a longer time, and to assess its long-term effects on plant water status, growth, productivity and fruit yield and quality as well as soil health. Assessments of the long-term effects of mulches over more years and at more sites with uniform cherry varieties and tree ages would also be useful at these areas. Overall, as strength, this project was initiated to address key research questions on commercial sweet cherry orchards that are located in the northern and central region of the Okanagan Valley. These orchards had different soil textures and agronomic managements, and were planted with different cultivars and rootstocks. 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Eur J Agron 31:36â42. doi: 10.1016/j.eja.2009.03.001 Zheljazkov VD, Vick BA, Baldwin BS, Buehring N, Coker C, Astatkie T, Johnson B (2011) Oil productivity and composition of sunflower as a function of hybrid and planting date. Ind Crops Prod 33:537â543. doi: 10.1016/j.indcrop.2010.11.004 Zunke U (1990) Observations on the Invasion and Endoparasitic Behavior of the Root Lesion Nematode Pratylenchus penetrans. J Nematol 22:309â20. doi: 10.1016/0022-1910(59)90030-7 118 Appendices Appendix A: Responses of average daily maximum and minimum soil moisture contents to organic amendment and irrigation treatments. Figure A1 Average daily maximum soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2016 growing season across the three sites. Each point is typically an average of three plots per site per day, except when data were missing because of data logger error. 119 Figure A2 Average daily maximum soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2017 growing season across the three sites. Each point is typically an average of three plots per site per day, except when data were missing because of data logger error. 120 Figure A3 Average daily minimum soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2016 growing season across the three sites. Each point is typically an average of three plots per site per day, except when data were missing because of data logger error. 121 Figure A4 Average daily minimum soil moisture measured in plots amended with compost or woodchips compared to bare ground (unamended plots) in the 2017 growing season across the three sites. Each point is typically an average of three plots per site per day, except when data were missing because of data logger error. 122 Figure A5 Average daily maximum (max) soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2016 at Sites 2 and 3. Each point is typically an average of four plots per site per day, except when data were missing because of data logger error. 123 Figure A5 Average daily maximum soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2017. PDI- treated plots had partial measurement at Site 1 as PDI was implemented only in 2017 (Aug â Sep). Each point is typically an average of four plots per site per day, except when data were missing because of data logger error. 124 Figure A6 Average daily minimum soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2016 at Sites 2 and 3. Each point is typically an average of four plots per site per day, except when data were missing because of data logger error. 125 Figure A7 Average daily minimum soil moisture contents measured in plots treated with postharvest deficit irrigation (PDI) and full irrigation (FI) in 2017. PDI- treated plots had partial measurement at Site 1 as PDI was implemented only in 2017 (Aug â Sep). Each point is typically an average of four plots per site per day, except when data were missing because of data logger error. Appendix B: Regression figures showing relationship between soil moisture content stem water potential Figure B1 Relationship of daily averaged volumetric 1. Every dot shows individual samplesrelationship was compared at P<0.05 soil water contents and stem water potential at Site from 2016 and 2017 growing seasons. The significance level of their . 126 and Figure B2 Relationship of daily averaged volumetric 2. Every dot shows individual samplesrelationship was compared at P<0.05 soil water contents and stem water potential at Site from 2016 and 2017 growing seasons. The significance level of their . 127 Figure B3 Relationship of daily averaged volumetric 3. Every dot shows individual samplesrelationship was compared at P<0.05 soil water contents and stem water potential at Site from 2016 and 2017 growing seasons. The significance level of their . 128 129 Appendix C: Effects of mulches and postharvest deficit irrigation on soil chemical properties at each site Table C1 Effects of mulches and postharvest deficit irrigation (PDI) on key soil nutrients over two years and in each of 2016 and 2017 at Site 1. Mulches (2016-2017) POXC (mg kg-1) SE C (%) SE N (%) SE P (mg kg-1) SE OM (%) SE K (mg kg-1) SE Mg (mg kg-1) SE Bare (n=12) 859.96b 60.54 4.89b 0.15 0.44b 0.01 114.56b 7.12 9.81b 0.25 435.08b 30.63 341.47b 18.53 Compost (n=12) 934.24a 60.63 5.99a 0.15 0.52a 0.01 148.36a 7.12 11.20a 0.25 1391.34a 30.63 540.88a 19.22 Woodchips (n=12) 902.05a 60.54 5.07b 0.15 0.44b 0.01 102.43b 7.12 10.12b 0.25 404.01b 31.99 318.23b 18.53 ANOVA F P F P F P F P F P F P F P Mulch (df=2) 6.75 0.003 19.17 <0.001 15.97 <0.001 11.18 <0.001 15.94 <0.001 330.51 <0.001 53.17 <0.001 Mulches (2016) Bare (n=12) 1062.00 22.29 4.98b 0.14 0.46ab 0.02 139.13a 9.11 9.69 0.25 528.13b 34.46 370.28b 14.75 Compost (n=12) 1110.00 22.29 5.36a 0.14 0.48a 0.02 128.33ab 9.11 10.16 0.25 1335.00a 34.46 443.13a 14.75 Mulch (n=12) 1110.00 22.29 5.07ab 0.14 0.44b 0.02 108.11b 9.11 9.87 0.25 459.45b 34.46 318.10c 14.75 ANOVA F P F P F P F P F P F P F P mulch (df =2) 1.55 0.238 3.89 0.037 3.28 0.059 4.31 0.028 1.87 0.180 199.65 <0.001 32.93 <0.001 Mulches (2017) Bare (n=12) 657.92b 19.66 4.81b 0.21 0.42b 0.02 89.99b 8.27 9.94b 0.36 342.03b 23.57 312.66b 26.45 Compost (n=12) 780.00a 19.66 6.63a 0.21 0.59a 0.02 168.39a 8.27 12.90a 0.36 1437.53a 25.81 697.07a 26.45 Mulch (n=12) 694.12b 19.66 5.07b 0.21 0.43b 0.02 96.74b 8.27 10.37b 0.36 337.78b 23.57 318.36b 26.45 Irrigation (Irrig) PDI (n=6) 720.64 16.36 5.51 0.19 0.48 0.02 120.51 6.75 11.21 0.37 650.07b 20.29 439.18 24.53 Full (n=6) 700.72 16.36 5.50 0.19 0.48 0.02 116.24 6.75 10.93 0.37 761.49a 20.29 446.21 24.53 ANOVA F P F P F P F P F P F Pâ¡ F P Irrig (df = 1) 0.74 0.428 0.00 0.984 0.01 0.933 0.20 0.673 0.38 0.567 17.22 0.009 0.06 0.823 MULCH (df = 2) 10.36 <0.001 25.43 <0.001 27.12 <0.001 27.59 <0.001 36.22 <0.001 964.69 <0.001 101.08 <0.001 Irrig*mulch(df=2) 1.98 0.164 0.17 0.846 0.16 0.857 0.02 0.978 1.91 0.174 1.01 0.383 0.72 0.500 The nutrients include POXC (permanganate oxidizable carbon), N (total nitrogen), C (total carbon), OM (organic matter), K (potassium) and Mg (magnesium). Irrigation treatment was applied for 2017 experiment. Full â full irrigation (control). Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 130 Table C2 Effects of mulches and postharvest deficit irrigation on different forms of carbon and nitrogen as well as C:N over two seasons and in 2016 and 2017 at Site 1. Mulches (2016-2017) InoC (%) SE OrgC (mg kg-1) SE C:N SE NO3 (%) SE NH4 (%) SE Bare (n=12) 0.19 0.04 442.69b 42.68 11.18b 0.12 71.07a 9.51 9.84a 1.61 Compost (n=12) 0.19 0.04 809.01a 43.33 11.20b 0.12 54.33b 9.38 2.22b 1.61 Woodchips (n=12) 0.19 0.04 474.16b 42.68 11.67a 0.12 25.61c 9.38 1.76b 1.61 ANOVA F P F P F P F P F P MULCH (df = 2) 0.01 0.989 72.01 <0.001 8.62 0.001 21.78 <0.001 10.76 <0.001 Mulches (2016) Bare (n=12) 0.08 0.01 378.76b 17.17 10.98b 0.22 104.98a 11.74 18.36a 2.21 Compost (n=12) 0.07 0.01 594.27a 17.17 11.20b 0.22 76.53b 11.36 2.87b 2.21 Woodchips (n=12) 0.07 0.01 387.12b 17.17 11.50a 0.22 36.97c 11.36 2.49b 2.21 ANOVA F Pâ¡ F P F P F Pâ¡ F P mulch (df = 2) 0.18 0.834 52.86 <0.001 9.23 0.001 29.96 <0.001 17.18 <0.001 Mulches (2017) Bare (n=12) 0.31 0.01 506.62b 43.45 11.37b 0.11 38.92a 6.35 0.99b 0.11 Compost (n=12) 0.32 0.01 1112.02a 43.45 11.21b 0.11 32.13a 6.35 1.57a 0.11 Woodchips (n=12) 0.31 0.01 561.19b 43.45 11.85a 0.11 14.25b 6.35 1.02b 0.11 Irrigation (Irrig) PDI (n=6) 0.31 0.01 719.37 41.70 11.45 0.13 27.07 5.48 1.28 0.09 Full (n=6) 0.31 0.01 733.85 41.70 11.50 0.13 29.80 5.48 1.11 0.10 ANOVA F P F P F P F P F P Irrig (df =1) 0.00 0.970 0.07 0.796 0.11 0.755 0.18 0.687 2.32 0.188 mulch (df =2) 0.53 0.595 87.28 <0.001 24.59 <0.001 5.30 0.014 11.79 0.001 Irrig*mulch (df=2) 5.15 0.016 0.12 0.887 1.08 0.359 0.45 0.642 0.25 0.785 Irrigation treatment was applied for 2017 experiment. Full â full irrigation (control).Data expressed as mean and standard errors (SE). Mulching sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. Carbon types: InoC â inorganic C and OrgC â organic C ; nitrogen types: NO3- â nitrate and NH4+ â ammonium. â¡ =data was log-transformed for analysis 131 Table C3 Effects of mulches and postharvest deficit irrigation (PDI) on soil pH, EC, CEC and Al, Ca, Mn and Na over two seasons and in 2016 and 2017 at Site 1. Mulches (2016-2017) Al (mg kg-1) SE Ca (mg kg-1) SE Mn (mg kg-1) SE Na (mg kg-1) SE pH SE EC (uS cm-1) SE CEC (meq 100 g-1) SE Bare (n=12) 2.61a 0.38 4215.40b 105.37 39.02a 3.73 43.93b 3.07 5.65b 0.07 54.42a 6.00 25.37b 0.76 Compost (n=12) 0.73b 0.38 4746.71a 105.37 11.52c 3.60 71.74a 3.07 6.41a 0.07 58.79a 6.00 32.35a 0.76 Woodchips (n=12) 2.49a 0.38 4191.30b 105.37 26.39b 3.60 43.24b 3.07 5.78b 0.07 33.35b 6.00 24.91b 0.76 ANOVA F P F P F P F P F P F P F P mulch (df = 2) 7.62 0.001 11.30 <0.001 28.70 <0.001 96.03 <0.001 38.6 <0.001 8.00 0.001 47.56 <0.001 Mulches (2016) Bare (n=12) 2.83a 0.56 4312.22 127.77 57.31a 5.38 35.30b 2.09 5.65b 0.09 71.19 9.53 26.35b 0.71 Compost (n=12) 0.99b 0.56 4498.10 127.77 17.26c 5.38 64.70a 2.09 6.18a 0.09 63.69 9.53 29.91a 0.71 Woodchips (n=12) 2.55ab 0.56 4183.83 127.77 33.42b 5.38 32.76b 2.09 5.80b 0.09 39.37 9.53 25.01b 0.71 ANOVA F P F P F P F P F P F P F P mulch (df = 2) 3.52 0.049 1.73 0.203 15.77 <0.001 100.94 <0.001 9.82 0.001 3.48 0.051 15.85 <0.001 Mulches (2017) Bare (n=12) 2.39a 0.44 4118.57b 165.58 26.39a 3.08 52.56b 3.18 5.65b 0.09 37.66b 5.12 24.39b 1.03 Compost (n=12) 0.46b 0.44 4995.32a 165.58 5.80b 3.08 78.77a 3.18 6.64a 0.09 53.88a 5.12 34.79a 1.03 Woodchips (n=12) 1.75a 0.46 4198.77b 165.58 19.36a 3.08 53.73b 3.18 5.75b 0.09 27.34c 5.12 24.81b 1.03 Irrigation (Irrig) PDI (n=6) 1.69 0.41 4430.15 178.78 18.74 2.52 59.76 3.17 6.02 0.07 36.38 4.73 27.85 1.12 Full (n=6) 1.37 0.40 4444.96 178.78 15.62 2.52 63.61 3.17 6.01 0.07 42.88 4.73 28.14 1.12 ANOVA F P F P F P F P F P F P F P Irrig (df=1) 0.32 0.594 0.00 0.956 0.77 0.421 0.74 0.429 0.01 0.919 2.75 0.158 0.03 0.864 mulch (df=2) 5.51 0.013 13.70 <0.001 11.51 0.001 28.71 <0.001 40.40 <0.001 15.56 <0.001 54.22 <0.001 Irrig*mulch(df=2) 1.66 0.216 0.22 0.804 1.08 0.357 0.34 0.715 0.34 0.713 0.24 0.791 0.45 0.644 Irrigation treatment was applied for 2017 experiment. Full â full irrigation (control). Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. 132 Table C4 Effects of mulches and postharvest deficit irrigation on key soil nutrients over two seasons and in each of 2016 and 2017 at Site 2. Mulches (2016-2017) POXC (mg kg-1) SE C (%) SE N (%) SE P (mg kg-1) SE OM (%) SE K (mg kg-1) SE Mg (mg kg-1) SE Bare (n=12) 849.89 43.16 4.02c 0.08 0.33b 0.01 203.84ab 7.86 9.29 0.47 422.75b 31.58 329.60b 7.16 Compost (n=12) 881.26 43.16 4.42a 0.08 0.37a 0.01 209.45a 7.86 9.50 0.47 1236.05a 31.58 400.52a 7.65 Woodchips (n=12) 855.42 43.16 4.19b 0.08 0.33b 0.01 192.21b 7.86 9.34 0.47 476.98b 31.58 326.96b 7.16 Irrigation treatment (Irrig) PDI (n=6) 857.50 43.48 4.24 0.08 0.34 0.01 203.86 9.68 9.34 0.46 702.71 28.43 352.56 6.67 Full (n=6 ) 866.88 43.48 4.18 0.08 0.34 0.01 199.80 9.68 9.41 0.46 721.14 28.43 352.16 6.58 ANOVA F P F P F P F P F Pâ¡ F P F P Irrig (df =1) 0.13 0.729 0.36 0.562 0.06 0.819 0.09 0.772 0.13 0.729 0.29 0.603 0.00 0.967 mulch (df =2) 1.28 0.288 13.20 <0.001 21.66 <0.001 3.45 0.040 0.45 0.641 284.50 <0.001 34.77 <0.001 Irrig*mulch(df=2) 0.04 0.961 4.28 0.020 6.02 0.005 1.86 0.167 3.15 0.053 0.06 0.942 0.37 0.691 Mulches (2016) Bare (n=12) 990.00 23.60 4.08 0.07 0.33b 0.01 210.27 10.39 7.84 0.16 438.80b 54.76 341.41b 8.34 Compost (n=12) 984.00 23.60 4.19 0.07 0.34a 0.01 209.07 10.39 8.07 0.16 1169.46a 54.76 378.82a 8.34 Woodchips (n=12) 978.00 23.60 4.10 0.07 0.32b 0.01 203.23 10.39 7.99 0.16 494.07b 54.76 335.08b 8.34 Irrigation treatment (Irrig) PDI (n=6) 972.00 23.80 4.11 0.08 0.33 0.01 210.20 12.65 7.92 0.18 681.28 51.83 350.52 8.84 Full (n=6) 996.00 23.80 4.14 0.08 0.33 0.01 204.84 12.65 8.02 0.18 720.27 51.83 353.02 8.84 ANOVA F P F P F P F P F P F P F P Irrig (df = 1) 0.51 0.508 0.11 0.756 0.00 1.000 0.09 0.777 0.19 0.678 0.48 0.519 0.04 0.849 mulch (df =2) 0.09 0.916 1.78 0.194 4.75 0.021 0.34 0.717 1.74 0.201 100.17 <0.001 12.22 <0.001 Irrig*mulch(df=2) 0.09 0.916 2.35 0.122 2.69 0.092 0.47 0.630 2.33 0.123 0.08 0.924 1.11 0.350 Mulches (2017) Bare (n=12) 709.77b 42.17 3.96c 0.12 0.34b 0.01 197.41b 13.21 11.18 0.54 528.13b 34.46 370.28b 14.75 Compost (n=12) 778.53a 42.17 4.65a 0.12 0.39a 0.01 209.83a 13.21 10.93 0.54 1335.00a 34.46 443.13a 14.75 Woodchips (n=12) 732.85b 42.17 4.28b 0.12 0.33b 0.01 181.19b 13.21 11.10 0.54 459.45b 34.46 318.10c 14.75 Irrigation treatment (Irrig) PDI (n=6) 743.00 42.38 4.37 0.13 0.36 0.01 197.53 15.47 11.06 0.44 792.03 28.13 392.80a 13.74 Full (n=6) 737.76 42.38 4.22 0.13 0.35 0.01 194.76 15.47 11.08 0.44 756.35 28.13 361.54b 13.74 ANOVA F P F P F P F P F P F P F P Irrig (df = 1) 0.02 0.891 0.90 0.385 0.09 0.781 0.02 0.893 0.00 0.977 0.80 0.411 4.77 0.081 mulch (df = 2) 2.60 0.099 17.53 <0.001 23.99 <0.001 4.38 0.026 0.06 0.943 199.65 <0.001 32.93 <0.001 Irrig*mulch(df=2) 0.19 0.826 3.99 0.035 4.92 0.018 1.98 0.164 0.52 0.601 2.78 0.086 0.09 0.918 133 The nutrients include POXC (permanganate oxidizable carbon), N (nitrogen), C (carbon), OM (organic matter), K (potassium) and Mg (magnesium). Organic materials include compost and woodchips along with bare ground as a control. Postharvest deficit irrigation treatments are DI â deficit irrigation and Full â full irrigation (control). Data expressed as mean and standard errors (SE). Mulching sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 134 Table C5 Effects of soil mulches and postharvest deficit irrigation on different forms of carbon and nitrogen as well as C:N over two seasons and in 2016 and 2017 at Site 2. Mulches (2016-2017) InoC (%) SE OrgC (mg kg-1) SE C:N SE NO3 (%) SE NH4 (%) SE Bare (n=12) 0.18b 0.04 536.59 72.21 11.97 0.14 46.23a 7.15 0.85b 0.13 Compost (n=12) 0.19b 0.04 561.69 72.21 11.92 0.14 41.54a 7.15 1.14a 0.13 Woodchips (n=12) 0.20a 0.04 514.90 72.21 12.02 0.14 11.32b 7.15 0.95ab 0.13 Irrigation (Irrig) PDI (n=6) 0.200 0.04 528.00 66.00 11.94 0.12 31.44 6.32 1.05 0.12 Full (n=6 ) 0.188 0.04 547.45 66.00 12.00 0.12 34.62 6.32 0.91 0.12 ANOVA$ F Pâ¡ F Pâ¡ F P F Pâ¡ F P Irrig (df = 1) 2.40 0.150 0.29 0.601 0.14 0.722 0.09 0.764 1.45 0.254 mulch (df = 2) 4.08 0.024 2.16 0.127 0.10 0.903 47.60 <0.001 3.24 0.049 Irrig*mulch (df =2) 0.22 0.800 0.27 0.764 0.72 0.492 0.46 0.634 0.28 0.754 Mulches (2016) Organic amendment (OA) Bare (n=12) 0.07b 0.01 275.86b 8.89 12.46ab 0.17 21.16b 4.49 0.67b 0.23 Compost (n=12) 0.06b 0.01 458.62a 9.30 12.26b 0.17 38.52a 4.49 1.13a 0.22 Woodchips (n=12) 0.10a 0.01 297.27b 8.89 12.66a 0.17 7.27c 4.49 1.00a 0.22 Irrigation (Irrig) PDI (n=6) 0.09 0.01 338.92 8.47 12.43 0.18 24.82 3.97 0.97 0.23 Full (n=6) 0.07 0.01 348.92 8.66 12.49 0.18 19.81 3.97 0.89 0.23 ANOVA F Pâ¡ F P F P F Pâ¡ F P Irrig (df = 1) 2.80 0.155 0.68 0.447 0.09 0.777 0.45 0.530 0.10 0.761 mulch (df = 2) 5.63 0.012 143.09 <0.001 5.23 0.015 48.21 <0.001 3.05 0.071 Irrig*mulch (df =2) 0.48 0.624 0.11 0.900 1.91 0.173 1.01 0.383 0.55 0.586 Mulches (2017) Bare (n=12) 0.30 0.01 797.31 93.04 11.48 0.14 50.52a 7.63 1.01 0.12 Compost (n=12) 0.33 0.01 650.01 93.04 11.58 0.14 44.57a 7.13 1.15 0.12 Woodchips (n=12) 0.32 0.01 732.52 93.04 11.37 0.14 15.38b 7.13 0.91 0.12 Irrigation (Irrig) PDI (n=6) 0.32 0.01 717.09 75.96 11.44 0.13 32.48 6.40 1.14a 0.12 Full (n=6) 0.31 0.01 736.14 75.96 11.51 0.13 41.16 6.40 0.91b 0.12 ANOVA F P F P F P F P F P 135 Mulches (2016-2017) InoC (%) SE OrgC (mg kg-1) SE C:N SE NO3 (%) SE NH4 (%) SE Irrig (df = 1) 0.27 0.625 0.03 0.866 0.20 0.674 1.52 0.272 4.17 0.097 mulch (df = 2) 1.02 0.378 0.63 0.543 0.80 0.465 9.74 0.001 2.13 0.146 Irrig*mulch (df =2) 0.08 0.924 0.33 0.724 1.07 0.363 0.08 0.920 1.81 0.190 Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 and P â¤ 0.1 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 136 Table C6 Effects of mulches and PDI on soil pH, EC, CEC as well as Al, Ca, Mn and Na over two seasons and in 2016 and 2017 at Site 2. Mulches (2016-2017) Al (mg kg-1) SE Ca (mg kg-1) SE Mn (mg kg-1) SE Na (mg kg-1) SE pH SE EC (uS cm-1) SE CEC (meq 100 g-1) SE Bare (n=12) 0.57 0.08 4992.72 59.70 4.65a 0.33 37.46b 1.41 6.51 0.17 29.35b 2.99 28.94b 0.40 Compost (n=12) 0.56 0.08 4964.67 59.70 3.03b 0.33 46.40a 1.41 7.02 0.17 38.72a 2.99 31.69a 0.40 Woodchips (n=12) 0.57 0.08 5041.63 59.70 4.33a 0.33 29.15c 1.41 6.93 0.17 20.44c 2.99 29.27b 0.40 Irrigation (Irrig) PDI (n=6) 0.62 0.07 4982.64 54.88 4.27 0.33 37.17 1.22 6.72 0.14 28.52 2.50 29.88 0.41 Full (n=6 ) 0.52 0.07 5016.71 54.88 3.74 0.33 38.17 1.22 6.92 0.14 30.48 2.50 30.05 0.41 ANOVA F P F P F Pâ¡ F P F Pâ¡ F P F P Irrig (df = 1) 1.53 0.241 0.53 0.484 1.13 0.311 0.50 0.494 1.04 0.330 0.31 0.591 0.12 0.745 mulch (df = 2) 0.01 0.994 0.92 0.408 16.51 <0.001 50.06 <0.001 1.28 0.287 9.60 <0.001 28.89 <0.001 Irrig*mulch(df=2) 0.60 0.551 7.51 0.002 0.49 0.613 0.30 0.744 1.09 0.345 0.33 0.724 4.77 0.012 Mulches (2016) Bare (n=12) 0.71 0.11 5143.65a 64.79 4.20 0.51 35.96b 1.76 6.40 0.33 24.43b 4.00 29.83b 0.36 Compost (n=12) 0.81 0.11 4988.98b 64.79 3.16 0.51 51.88a 1.76 7.07 0.33 43.23a 4.00 31.30a 0.36 Woodchips(n=12) 0.67 0.11 5122.62a 64.79 3.88 0.51 29.62c 1.76 7.05 0.33 20.27b 4.00 29.78b 0.36 Irrigation (Irrig) PDI (n=6) 0.79 0.09 5055.38 61.41 4.03 0.50 38.01 1.74 6.60 0.27 28.34 3.27 30.09 0.37 Full (n=6) 0.67 0.09 5114.79 61.41 3.46 0.50 40.30 1.74 7.08 0.27 30.28 3.27 30.52 0.37 ANOVA F P F P F P F P F Pâ¡ F Pâ¡ F P Irrig (df = 1) 0.86 0.396 0.92 0.383 0.94 0.377 1.36 0.297 1.11 0.341 0.19 0.681 0.80 0.412 mulch (df = 2) 0.41 0.671 3.38 0.055 1.97 0.166 84.34 <0.001 1.07 0.363 12.85 <0.001 10.05 0.001 Irrig*mulch(df=2) 0.28 0.759 5.36 0.014 0.93 0.412 0.50 0.614 1.05 0.368 0.29 0.748 3.90 0.037 Mulches (2017) Bare (n=12) 2.83a 0.56 4312.22 127.77 57.31a 5.38 35.30b 2.09 6.63b 0.09 34.27a 4.30 26.35b 0.71 Compost (n=12) 0.99b 0.56 4498.10 127.77 17.26c 5.38 64.70a 2.09 6.97a 0.09 34.21a 4.30 29.91a 0.71 Woodchips(n=12) 2.55ab 0.56 4183.83 127.77 33.42b 5.38 32.76b 2.09 6.82a 0.09 20.60b 4.30 25.01b 0.71 Irrigation (Irrig) PDI (n=6) 2.38 0.47 4303.00 115.80 36.70 4.52 45.25 1.82 6.84 0.09 28.71 4.26 27.13 0.68 Full (n=6) 1.87 0.47 4359.77 115.80 35.30 4.52 43.26 1.82 6.77 0.09 30.68 4.26 27.05 0.68 ANOVA F P F P F P F P F P F P F P Irrig (df = 1) 0.70 0.442 0.12 0.743 0.06 0.821 0.95 0.374 0.64 0.461 0.11 0.757 0.01 0.933 mulch (df =2) 3.52 0.049 1.73 0.203 15.77 <0.001 100.94 <0.001 9.01 0.002 4.40 0.026 15.85 <0.001 137 Mulches (2016-2017) Al (mg kg-1) SE Ca (mg kg-1) SE Mn (mg kg-1) SE Na (mg kg-1) SE pH SE EC (uS cm-1) SE CEC (meq 100 g-1) SE Irrig*mulch(df=2) 0.75 0.487 0.43 0.655 0.39 0.685 1.00 0.385 0.38 0.690 1.90 0.175 0.30 0.746 Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 138 Table C7 Effects of mulches and postharvest deficit irrigation (PDI) on key soil nutrients over two seasons and in each of 2016 and 2017 at Site 3. Mulches (2016-2017) POXC (mg kg-1) SE C (%) SE N (%) SE P (mg kg-1) SE OM (%) SE K (mg kg-1) SE Mg (mg kg-1) SE Bare (n=12) 700.88b 49.18 2.31b 0.17 0.19b 0.02 81.28b 10.71 4.89b 0.33 240.65b 22.65 256.27b 28.26 Compost (n=12) 793.05a 49.44 3.05a 0.17 0.28a 0.02 137.29a 10.71 6.16a 0.34 967.42a 22.97 425.06a 28.26 Woodchips(n=12) 699.81b 49.44 2.44b 0.17 0.19b 0.02 84.33b 10.71 5.19b 0.33 250.88b 22.97 247.36b 28.26 Irrigation treatment (Irrig) PDI (n=6) 749.66 47.65 2.68 0.17 0.23 0.02 103.65 9.66 5.55 0.35 507.67a 20.58 308.10 25.90 Full (n=6) 712.83 47.65 2.52 0.17 0.21 0.02 98.29 9.66 5.27 0.35 464.96b 20.58 311.03 25.90 ANOVA F P F P F Pâ¡ F Pâ¡ F P F P F Pâ¡ Irrig (df = 1) 2.06 0.179 0.74 0.408 1.33 0.274 2.03 0.182 0.70 0.422 4.60 0.055 0.09 0.765 mulch (df = 2) 5.74 0.006 15.12 <0.001 26.76 <0.001 19.17 <0.001 16.22 <0.001 579.11 <0.001 33.98 <0.001 Irrig*mulch (df=2) 1.03 0.365 0.14 0.867 0.53 0.594 1.29 0.286 0.48 0.624 1.20 0.312 0.07 0.934 Mulches (2016 ) Bare (n=12) 840.00 57.74 2.19b 0.19 0.18b 0.01 79.13b 10.74 4.62b 0.39 261.52b 35.29 238.07b 25.97 Compost (n=12) 936.00 57.74 2.64a 0.19 0.21a 0.01 117.37a 10.74 5.32a 0.39 963.71a 35.29 277.51a 26.25 Woodchips(n=12) 810.00 57.74 2.17b 0.19 0.17b 0.01 74.79b 10.74 4.68b 0.39 266.85b 35.29 219.17b 25.97 Irrigation treatment (Irrig) DI (n=6) 888.00 52.84 2.40 0.20 0.19 0.01 92.23 8.98 4.94 0.41 510.24 31.06 244.46 25.85 Full (n=6) 836.00 52.84 2.26 0.20 0.18 0.01 88.63 8.98 4.80 0.41 484.47 31.06 245.38 25.85 ANOVA F P F P F P F Pâ¡ F P F P F P Irrig (df = 1) 1.13 0.337 0.40 0.556 0.04 0.855 0.67 0.451 0.12 0.747 0.59 0.476 0.01 0.938 mulch (df = 2) 2.53 0.105 4.49 0.025 6.04 0.009 6.59 0.006 3.49 0.050 194.00 <0.001 12.90 <0.001 Irrig*mulch (df=2) 0.01 0.993 0.01 0.988 2.49 0.110 0.65 0.531 0.15 0.860 0.38 0.688 0.78 0.473 Mulches (2017) Bare (n=12) 561.76b 41.50 2.44b 0.28 0.21b 0.02 83.43b 18.33 5.49b 0.56 219.77c 27.70 274.47b 36.86 Compost (n=12) 690.40a 41.50 3.87a 0.28 0.33a 0.02 157.21a 18.33 7.62a 0.56 921.93a 28.52 535.01a 36.86 Woodchips(n=12) 587.18b 42.62 2.71b 0.28 0.21b 0.02 93.87b 18.33 5.69b 0.56 236.68b 28.52 275.56b 36.86 Irrigation treatment (Irrig) PDI (n=6) 638.19 40.77 3.07 0.27 0.26 0.02 115.06 16.78 6.35 0.58 487.07 25.27 354.36 32.44 Full (n=6) 588.04 41.28 2.94 0.27 0.24 0.02 107.95 16.78 6.18 0.58 431.85 26.04 368.99 32.44 ANOVA F P F P F P F Pâ¡ F Pâ¡ F Pâ¡ F P Irrig (df =1) 1.40 0.290 0.19 0.683 0.45 0.532 1.27 0.311 0.18 0.693 0.00 0.970 0.17 0.693 139 Mulches (2016-2017) POXC (mg kg-1) SE C (%) SE N (%) SE P (mg kg-1) SE OM (%) SE K (mg kg-1) SE Mg (mg kg-1) SE mulch (df = 2) 6.16 0.009 13.26 <0.001 17.47 <0.001 11.70 <0.001 11.11 0.001 131.62 <0.001 24.56 <0.001 Irrig*mulch (df=2) 0.76 0.481 0.21 0.811 0.20 0.820 0.60 0.558 0.24 0.785 2.57 0.103 0.01 0.989 The nutrients include POXC (permanganate oxidizable carbon), TN (total nitrogen), TC (total carbon), OM (organic matter), K (potassium) and Mg (magnesium). Full â full irrigation (control). Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 140 Table C8 Effects of mulches and postharvest deficit irrigation (PDI) on different forms of C and Nas well as C:N over two seasons and in 2016 and 2017 at Site 3. Mulches (2016-2017) InoC (%) SE OrgC (mg kg-1) SE C:N SE NO3 (%) SE NH4 (%) SE Bare (n=12) 0.13b 0.03 315.42b 35.08 11.93b 0.16 11.07a 0.96 0.77a 0.12 Compost (n=12) 0.18a 0.03 739.78a 35.08 11.73b 0.16 13.31a 0.96 0.97a 0.12 Woodchips (n=12) 0.12b 0.03 332.46b 35.08 12.65a 0.16 5.30b 0.96 0.47b 0.12 Irrigation (Irrig) PDI (n=6) 0.15 0.03 485.87 32.13 12.07 0.13 9.84 0.88 0.71 0.11 Full (n=6) 0.13 0.03 439.23 32.13 12.14 0.13 9.94 0.88 0.76 0.11 ANOVA F P F P F Pâ¡ F Pâ¡ F Pâ¡ Irrig (df = 1) 0.96 0.348 2.74 0.126 0.56 0.488 0.02 0.898 0.24 0.636 mulch (df = 2) 10.56 <0.001 97.00 <0.001 17.16 <0.001 40.48 <0.001 8.22 0.001 Irrig*mulch (df =2) 0.70 0.501 0.47 0.628 0.27 0.766 0.92 0.405 0.93 0.404 Mulches (2016) Bare (n=12) 0.06 0.01 310.83b 28.07 12.12ab 0.15 11.72b 1.53 0.81ab 0.14 Compost (n=12) 0.07 0.01 582.52a 28.84 11.88b 0.15 16.86a 1.53 1.07a 0.14 Woodchips (n=12) 0.06 0.01 292.28b 28.07 12.39a 0.15 4.26c 1.53 0.51b 0.14 Irrigation treatment (Irrig) PDI (n=6) 0.06 0.01 405.00 26.09 12.22 0.16 11.19 1.41 0.85 0.13 Full (n=6) 0.06 0.01 385.41 25.72 12.03 0.17 10.70 1.41 0.75 0.13 ANOVA F P F P F P F P F P Irrig (df = 1) 0.30 0.610 0.73 0.432 0.66 0.452 0.06 0.814 0.69 0.443 mulch (df = 2) 0.41 0.669 64.48 <0.001 6.24 0.008 19.78 <0.001 6.89 0.005 Irrig*mulch (df =2) 0.89 0.427 2.86 0.082 0.37 0.693 0.61 0.555 0.02 0.977 Mulches (2017) Bare (n=12) 0.19b 0.02 320.01c 50.84 11.72b 0.18 10.42a 0.83 0.72b 0.20 Compost (n=12) 0.29a 0.02 865.06a 50.84 11.59b 0.17 9.75a 0.83 0.86a 0.20 Woodchips (n=12) 0.18b 0.02 372.63b 50.84 12.91a 0.17 6.34b 0.83 0.43b 0.20 Irrigation treatment (Irrig) PDI (n=6) 0.23 0.02 545.42 46.44 12.14 0.16 8.49 0.95 0.57 0.20 141 Mulches (2016-2017) InoC (%) SE OrgC (mg kg-1) SE C:N SE NO3 (%) SE NH4 (%) SE Full (n=6) 0.21 0.02 493.05 46.44 12.01 0.16 9.18 0.95 0.77 0.20 ANOVA F Pâ¡ F Pâ¡ F P F P F Pâ¡ Irrig (df = 1) 0.70 0.442 2.14 0.204 1.25 0.314 0.26 0.630 0.00 0.988 mulch (df = 2) 20.69 <0.001 96.04 <0.001 51.56 <0.001 13.22 <0.001 6.71 0.006 Irrig*mulch (df =2) 0.44 0.647 0.79 0.467 2.00 0.164 0.61 0.553 0.15 0.863 Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 142 Table C9 Effects of mulches and postharvest deficit irrigation (PDI) on soil pH, EC, CEC as well as Al, Ca, Mn and Na over two seasons and in 2016 and 2017 at Site 3. Mulches (2016-2017) Al (mg kg-1) SE Ca (mg kg-1) SE Mn (mg kg-1) SE Na (mg kg-1) SE pH SE EC (uS cm-1) SE CEC (meq 100 g-1) SE Bare (n=12) 0.64 0.12 2311.67b 146.34 5.46b 0.31 9.99b 0.96 6.57b 0.05 12.14b 1.22 14.35b 1.19 Compost (n=12) 0.63 0.12 2727.08a 146.34 2.65c 0.31 26.13a 0.96 7.15a 0.05 25.97a 1.22 19.70a 1.19 Woodchips(n=12) 0.79 0.12 2263.09b 146.34 6.43a 0.31 9.99b 0.96 6.54b 0.05 10.15b 1.22 14.13b 1.19 Irrigation (Irrig) PDI (n=6) 0.75 0.11 2420.78 136.12 4.84 0.27 15.78 0.81 6.74 0.05 16.40 1.03 16.01 1.14 Full (n=6) 0.62 0.11 2447.11 136.12 4.86 0.27 14.96 0.81 6.77 0.05 15.77 1.03 16.12 1.14 ANOVA$ F P F Pâ¡ F Pâ¡ F Pâ¡ F P F Pâ¡ F Pâ¡ Irrig (df = 1) 0.43 0.525 0.02 0.900 0.01 0.910 0.10 0.764 0.49 0.498 0.70 0.421 0.00 0.960 mulch (df = 2) 0.34 0.716 8.49 0.001 79.28 <0.001 135.70 <0.001 81.3 <0.001 80.14 <0.001 25.26 <0.001 Irrig*mulch (df =2) 2.18 0.125 0.30 0.742 2.15 0.129 2.17 0.126 0.86 0.429 0.21 0.809 0.19 0.829 Mulches (2016) Bare (n=12) 0.72 0.20 2176.99 151.58 4.91b 0.35 9.50b 1.23 6.66b 0.06 13.20b 1.67 13.58b 1.05 Compost (n=12) 0.74 0.20 2343.94 151.58 2.93c 0.35 28.56a 1.23 7.17a 0.06 30.27a 1.67 16.92a 1.05 Woodchips(n=12) 0.87 0.20 2172.90 151.58 5.99a 0.35 9.29b 1.23 6.67b 0.06 10.20b 1.67 13.42b 1.05 Irrigation (Irrig) PDI (n=6) 0.82 0.20 2255.39 155.10 4.61 0.38 15.88 1.07 6.82 0.05 17.86 1.36 14.83 1.06 Full (n=6) 0.74 0.20 2207.16 155.10 4.61 0.38 15.69 1.07 6.85 0.05 17.92 1.36 14.45 1.06 ANOVA F Pâ¡ F P F P F Pâ¡ F P F P F P Irrig (df = 1) 0.08 0.790 0.11 0.749 0.00 0.998 0.07 0.802 0.21 0.662 0.20 0.677 0.11 0.752 mulch (df = 2) 0.10 0.902 1.58 0.231 40.02 <0.001 173.98 <0.001 36.7 <0.001 54.19 <0.001 14.08 <0.001 Irrig*mulch (df =2) 4.54 0.024 0.39 0.679 0.56 0.581 0.86 0.438 0.38 0.690 1.19 0.325 0.35 0.707 Mulches (2017) Bare (n=12) 0.56a 0.04 2446.35b 225.09 6.02a 0.51 10.48b 1.43 6.48b 0.07 11.08b 1.44 15.13b 1.50 Compost (n=12) 0.40b 0.05 3110.22a 225.09 2.37b 0.51 23.70a 1.43 7.13a 0.07 21.68a 1.44 22.48a 1.50 Woodchips(n=12) 0.56a 0.05 2353.27b 225.09 6.88a 0.51 10.68b 1.43 6.40b 0.07 10.10b 1.44 14.84b 1.50 Irrigation (Irrig) PDI (n=6) 0.50 0.04 2586.16 205.08 5.07 0.42 15.67 1.25 6.65 0.07 14.95 1.35 17.19 1.36 Full (n=6) 0.51 0.04 2687.06 205.08 5.10 0.42 14.24 1.25 6.69 0.07 13.63 1.35 17.78 1.36 ANOVA F P F P F Pâ¡ F P F P F P F P 143 Mulches (2016-2017) Al (mg kg-1) SE Ca (mg kg-1) SE Mn (mg kg-1) SE Na (mg kg-1) SE pH SE EC (uS cm-1) SE CEC (meq 100 g-1) SE Irrig (df = 1) 0.71 0.11 0.30 0.609 0.04 0.856 0.77 0.420 0.25 0.639 0.83 0.404 0.23 0.654 mulch (df = 2) 0.72 0.11 6.60 0.006 47.52 <0.001 33.01 <0.001 47.2 <0.001 34.42 <0.001 15.96 <0.001 Irrig*mulch (df =2) 0.73 0.11 0.03 0.975 1.98 0.164 2.62 0.098 0.75 0.487 0.89 0.428 0.01 0.990 Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 144 Appendix D: Responses of soil biological properties to mulches and postharvest deficit irrigation at each site Table D1 Effects of mulching and postharvest deficit irrigation (PDI) on soil biological properties over two seasons and in 2016 and 2017 at Site 1. Mulches (2016-2017) AMF (%) SE P. penetrans (1g-1dry root) SE P. penetrans (50g-1 soil) SE Total Nematodes (50g-1 soil) SE FDA (µg g-1)SE Log (16S copy # g-1 dry soil) SE Log (18S copy # g-1 dry soil SE Bare (n=12) 11.18a 2.19 35.59 17.35 19.54 3.51 131.00 17.40 6.41 0.51 9.39 0.05 8.02 0.07 Compost (n=12) 5.66b 2.19 50.42 16.64 30.79 3.51 180.08 17.75 6.45 0.51 9.47 0.05 7.93 0.07 Woodchips(n=12) 8.82a 2.19 16.00 16.64 23.67 3.51 133.00 17.40 6.88 0.51 9.51 0.05 8.11 0.07 ANOVA F Pâ¡ F P F P F P F P F P F P mulch (df = 2) 5.24 0.011 1.28 0.288 2.63 0.084 3.00 0.060 1.70 0.195 1.35 0.270 3.17 0.052 Mulches (2016) Bare (n=12) 8.30 2.12 217.67 83.75 21.58 4.39 158.00 24.71 7.17 0.63 9.36 0.13 8.24a 0.11 Compost (n=12) 4.43 2.12 37.75 83.75 19.24 4.73 216.00 24.71 7.93 0.63 9.43 0.13 7.93b 0.11 Woodchips(n=12) 1.22 2.12 13.83 83.75 20.83 4.39 156.00 24.71 7.53 0.63 9.59 0.13 8.23a 0.11 ANOVA F P F Pâ¡ F P F Pâ¡ F P F P F P mulch (df = 2) 2.98 0.075 1.20 0.324 0.56 0.579 2.20 0.137 0.51 0.611 1.11 0.349 5.75 0.011 Mulches (2017) Bare (n=12) 14.07a 3.08 26.44 23.51 17.50 5.08 104.00 21.80 5.22b 0.45 9.24b 0.05 7.79b 0.07 Compost (n=12) 6.90b 3.08 63.06 23.51 35.42 5.08 168.00 21.80 4.98b 0.44 9.49a 0.05 7.94a 0.07 Woodchips(n=12) 16.42a 3.08 18.04 23.51 26.50 5.08 110.00 21.80 6.24a 0.44 9.41a 0.05 7.99a 0.07 Irrigation (Irrig) PDI (n=6) 14.97 2.53 49.14 23.32 29.83 4.14 133.33 17.81 5.27 0.46 9.37 0.05 7.89 0.07 Full (n=6) 9.95 2.53 22.55 23.32 23.11 4.14 121.33 17.81 5.68 0.46 9.39 0.05 7.93 0.07 ANOVA F Pâ¡ F P F P F P F P F P F P Irrig (df = 1) 0.46 0.527 0.65 0.457 1.32 0.303 0.23 0.654 0.94 0.376 0.07 0.805 0.49 0.514 mulch (df = 2) 4.21 0.030 1.36 0.280 3.11 0.066 2.63 0.097 10.96 <0.001 6.73 0.006 4.83 0.019 Irrig*mulch(df=2) 0.04 0.958 0.74 0.488 0.51 0.611 3.72 0.042 1.57 0.234 0.35 0.712 2.15 0.143 These properties include arbuscular mycorrhiza fungi (AMF) root colonization, abundance of root and soil nematodes (Pratylenchus spp.), microbial activity (fluorescein diacetate â FDA assay) and abundance of total bacteria (16S) and fungi (18S). Irrigation treatment was applied for 2017 experiment. Full â full irrigation (control). Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 145 Table D2 Effects of mulches and postharvest deficit irrigation (PDI) on soil biological properties over two seasons and in 2016 and 2017 at Site 2. Mulches (2016-2017) AMF (%) SE P. penetrans (1g-1dry root) SE P. penetrans (50g-1 soil) SE Total Nematodes (50g-1 soil) SE FDA (µg g-1) SE Log (16S copy # g-1 dry soil) SE Log (18S copy # g-1 dry soil SE Bare (n=12) 14.07a 1.64 123.38a 26.72 18.54b 5.17 137.00b 21.58 2.41 0.20 8.92b 0.09 7.28 0.18 Compost (n=12) 3.44b 1.53 46.46ab 26.72 13.08b 5.17 189.49ab 21.97 2.24 0.21 9.06a 0.09 7.34 0.18 Woodchips (n=12) 5.74b 1.53 21.25b 26.72 33.29a 5.17 221.50a 21.58 2.15 0.21 9.01a 0.09 7.35 0.18 Irrigation (Irrig) PDI (n=6) 8.55 1.29 29.92 22.86 16.97b 4.71 166.67 18.78 2.13 0.22 9.05 0.09 7.38 0.18 Full (n=6 ) 6.95 1.27 97.47 22.86 26.31a 4.71 198.66 18.98 2.40 0.22 8.94 0.09 7.26 0.18 ANOVA F P F P F P F P F P F P F P Irrig (df = 1) 0.78 0.397 3.06 0.108 4.77 0.052 2.12 0.1735 0.79 0.394 2.83 0.121 1.83 0.204 mulch (df = 2) 12.16 <0.001 4.06 0.024 7.97 0.001 5.23 0.0092 0.71 0.498 2.49 0.095 0.49 0.614 Irrig*mulch(df=2) 3.70 0.033 0.13 0.878 0.52 0.597 4.04 0.0247 0.67 0.519 3.56 0.037 4.09 0.024 Mulches (2016) Bare (n=12) 22.86a 2.84 83.08 36.01 21.42b 7.98 162.00b 31.57 2.96 0.34 8.60b 0.09 6.66 0.11 Compost (n=12) 2.81b 2.84 44.33 36.01 15.00b 7.98 170.00b 31.57 2.89 0.34 8.88a 0.09 6.80 0.11 Woodchips(n=12) 5.86b 2.84 16.67 36.01 46.75a 7.98 241.00a 31.57 2.67 0.34 8.79ab 0.09 6.80 0.11 Irrigation (Irrig) PDI (n=6) 11.61 2.32 25.89 30.53 23.00 7.30 169.33 30.70 2.96 0.29 8.87a 0.09 6.89 0.10 Full (n=6) 9.41 2.32 70.17 30.53 32.44 7.30 212.67 30.70 2.72 0.29 8.64b 0.09 6.62 0.10 ANOVA F Pâ¡ F Pâ¡ F Pâ¡ F P F P F P F P Irrig (df = 1) 0.52 0.503 0.11 0.752 0.17 0.695 1.59 0.2628 0.31 0.600 5.39 0.068 4.04 0.101 mulch (df = 2) 24.51 <0.001 2.33 0.123 6.96 0.005 3.61 0.0458 0.21 0.814 4.07 0.033 0.67 0.525 Irrig*mulch(df=2) 2.25 0.132 0.78 0.473 0.18 0.838 0.94 0.4084 1.62 0.223 4.89 0.019 4.06 0.033 Mulches (2017) Bare (n=12) 4.87 1.52 163.67 42.01 15.67 5.86 112.00b 25.02 1.86 0.18 9.24 0.04 7.90 0.07 Compost (n=12) 4.07 1.38 48.58 42.01 11.17 5.86 209.44a 25.69 1.83 0.18 9.23 0.04 7.89 0.07 Woodchips(n=12) 5.62 1.38 25.83 42.01 19.83 5.86 143.02b 26.66 1.84 0.18 9.23 0.04 7.90 0.07 Irrigation (Irrig) PDI (n=6) 4.45 1.17 33.94 36.01 10.94 5.19 164.00 22.98 1.60 0.19 9.22 0.03 7.88 0.07 Full (n=6) 5.25 1.17 124.78 36.01 20.17 5.19 145.64 24.05 2.09 0.19 9.24 0.03 7.91 0.07 ANOVA F P F Pâ¡ F Pâ¡ F P F P F P F P Irrig (df = 1) 0.24 0.647 3.08 0.140 3.23 0.132 0.76 0.4228 3.20 0.134 0.11 0.749 0.41 0.549 mulch (df = 2) 0.31 0.736 3.20 0.062 0.81 0.460 7.83 0.0039 0.01 0.987 0.01 0.988 0.05 0.955 146 Mulches (2016-2017) AMF (%) SE P. penetrans (1g-1dry root) SE P. penetrans (50g-1 soil) SE Total Nematodes (50g-1 soil) SE FDA (µg g-1) SE Log (16S copy # g-1 dry soil) SE Log (18S copy # g-1 dry soil SE Irrig*mulch(df=2) 3.36 0.058 0.57 0.572 0.81 0.460 1.95 0.1722 0.04 0.959 0.65 0.533 3.68 0.044 These properties include arbuscular mycorrhiza fungi (AMF) root colonization, abundance of root and soil nematodes, microbial activity (fluorescein diacetate (FDA) hydrolysis) and abundance of total bacteria (16S) and fungi (18S). Organic materials include compost and woodchips along with bare ground (control). Postharvest deficit irrigation treatments (Irrig) are PDI â postharvest deficit irrigation and Full â full irrigation (control). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis. 147 Table D3 Effects of mulches and postharvest deficit irrigation (PDI) on soil biological properties over two seasons and in 2016 and 2017 at Site 3. Mulches (2016-2017) AMF (%) SE P. penetrans (1g-1dry root) SE P. penetrans (50g-1 soil) SE Total Nematodes (50g-1 soil) SE FDA (µg g-1) SE Log (16S copy # g-1 dry soil) SE Log (18S copy # g-1 dry soil SE Bare (n=12) 14.69a 1.99 63.04 65.86 22.25 6.15 191.00 39.79 4.79 1.23 8.87b 0.09 7.20 0.19 Compost (n=12) 9.01b 1.99 17.21 65.86 18.50 6.15 159.50 39.79 5.25 1.23 9.02a 0.09 7.28 0.19 Woodchips (n=12) 10.98ab 1.99 143.71 65.86 23.08 6.15 139.50 39.79 6.16 1.23 8.97b 0.09 7.34 0.19 Irrigation (Irrig) PDI (n=6) 10.20 1.98 118.02 58.77 18.69 6.17 158.67 38.20 6.44a 1.28 8.96 0.09 7.30 0.19 Full (n=6) 12.93 1.98 31.28 58.77 23.86 6.17 168.00 38.20 4.36b 1.28 8.95 0.09 7.25 0.19 ANOVA F P F Pâ¡ F Pâ¡ F Pâ¡ F Pâ¡ F P F P Irrig (df = 1) 0.28 0.6093 1.23 0.297 0.17 0.691 0.18 0.6806 12.48 0.005 0.08 0.789 0.73 0.411 mulch (df = 2) 3.27 0.049 1.58 0.236 1.46 0.244 2.01 0.1474 2.22 0.121 4.74 0.014 2.44 0.099 Irrig*mulch(df=2) 1.42 0.2552 0.79 0.470 0.42 0.660 0.60 0.5554 1.18 0.318 0.72 0.492 0.42 0.658 Mulches (2016) Bare (n=12) 10.87 2.70 92.08 128.18 37.17 6.83 294.00 38.36 5.88 1.45 8.66 0.12 6.76 0.23 Compost (n=12) 8.58 2.70 28.75 128.18 34.25 6.83 286.00 38.36 5.03 1.47 8.74 0.12 6.77 0.23 Woodchips (n=12) 8.11 2.70 284.17 128.18 43.42 6.83 246.00 38.36 6.77 1.46 8.82 0.12 6.80 0.23 Irrigation (Irrig) PDI (n=6) 9.51 2.20 226.17 112.76 32.78 6.82 262.00 33.16 6.60 1.44 8.76 0.12 6.79 0.23 Full (n=6) 8.85 2.20 43.83 112.76 43.78 6.82 288.67 33.16 5.19 1.43 8.72 0.12 6.77 0.23 ANOVA F Pâ¡ F Pâ¡ F P F P F P F P F P Irrig (df = 1) 3.23 0.1324 0.26 0.638 1.41 0.289 0.48 0.520 6.41 0.052 0.50 0.511 0.04 0.857 mulch (df = 2) 1.22 0.324 0.84 0.462 0.67 0.521 0.59 0.562 3.21 0.066 2.28 0.128 0.10 0.910 Irrig*mulch(df=2) 1.58 0.239 0.27 0.770 0.12 0.885 0.26 0.774 0.27 0.769 0.82 0.454 2.43 0.114 Mulches (2017) Bare (n=12) 14.25 2.11 34.00a 12.54 7.33 1.75 88.00 20.97 3.69 0.29 9.07b 0.05 7.64 0.07 Compost (n=12) 9.44 1.94 5.66b 12.54 2.75 1.75 33.00 20.97 3.62 0.29 9.31a 0.05 7.80 0.07 Woodchips (n=12) 13.86 1.94 3.26b 12.54 2.75 1.75 33.00 20.97 4.03 0.29 9.13b 0.05 7.80 0.07 Irrigation (Irrig) PDI (n=6) 9.89 1.98 9.88 10.49 4.61 1.43 55.33 17.12 4.03 0.29 9.16 0.05 7.76 0.07 Full (n=6) 15.14 1.98 18.74 10.49 3.94 1.43 47.33 17.12 3.52 0.29 9.18 0.05 7.73 0.07 ANOVA F P F Pâ¡ F Pâ¡ F Pâ¡ F P F P F P 148 Mulches (2016-2017) AMF (%) SE P. penetrans (1g-1dry root) SE P. penetrans (50g-1 soil) SE Total Nematodes (50g-1 soil) SE FDA (µg g-1) SE Log (16S copy # g-1 dry soil) SE Log (18S copy # g-1 dry soil SE Irrig (df = 1) 3.51 0.120 0.44 0.537 1.00 0.363 0.00 0.969 2.26 0.193 0.03 0.875 0.09 0.781 mulch (df = 2) 2.43 0.116 3.95 0.036 1.00 0.386 0.30 0.7484 1.03 0.375 8.24 0.003 2.79 0.086 Irrig*mulch(df=2) 0.99 0.391 0.44 0.651 1.00 0.386 0.37 0.6982 0.52 0.602 1.68 0.213 1.31 0.292 These properties include arbuscular mycorrhiza fungi (AMF) root colonization, abundance of root and soil nematodes (Pratylenchus spp.), microbial activity (fluorescein diacetate â FDA assay) and abundance of total bacteria (16S) and fungi (18S). Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis. 149 Appendix E: Impacts of mulches and postharvest deficit irrigation (PDI) on TCSA, leaf area and cherry foliar nutrients Table E1 Effect of mulches and PDI on the tree trunk cross-sectional area (TCSA) and leaf area (LA) at three sites over two seasons together and in each season. Site 1 Site 2 Site 3 Mulches 2016-2017 TCSA (cm2) SE LA (cm2) SE TCSA (cm2) SE LA (cm2) SE TCSA (cm2) SE LA (cm2) SE Bare (n=12) 34.66 2.84 1840.47 61.38 72.06 2.38 1487.57 50.51 56.87b 4.17 1709.44b 39.63 Compost (n=12) 35.09 2.84 1849.74 61.38 73.60 2.38 1484.73 50.51 56.31b 4.17 1793.87a 39.63 Woodchips (n=12) 36.63 2.84 1860.19 61.38 71.64 2.38 1446.98 50.51 61.99a 4.17 1755.90a 39.63 ANOVA F P F P F P F P F P F P mulch (df=2) 1.35 0.279 0.03 0.968 0.47 0.634 0.29 0.755 2.94 0.074 2.73 0.088 Mulches (2016) Bare (n=12) 25.85 0.75 1961.97 52.96 66.74 2.04 1501.75 90.72 45.47 3.30 1765.54 60.08 Compost (n=12) 25.88 0.75 1961.10 51.35 66.85 2.04 1500.39 90.72 46.51 3.30 1880.13 60.08 Woodchips (n=12) 27.44 0.75 1969.36 51.35 65.71 2.04 1387.04 90.72 52.03 3.30 1809.26 60.08 ANOVA F P F P F P F Pâ¡ F P F Pâ¡ mulch (df=2) 1.49 0.272 0.01 0.992 0.10 0.909 0.89 0.441 2.36 0.120 1.90 0.201 Mulches (2017) Bare (n=12) 43.46 1.56 1722.89b 55.51 77.39 2.43 1473.39 55.12 68.26 4.57 1653.34 37.20 Compost (n=12) 44.30 1.56 1875.34a 58.41 80.35 2.43 1469.07 55.12 66.11 4.57 1707.60 37.20 Woodchips (n=12) 45.82 1.56 1751.03b 55.51 77.57 2.43 1506.93 55.12 71.94 4.57 1702.53 37.20 Irrigation (Irrig) PDI (n=6) 45.51 1.46 --- --- 79.55 2.27 1524.18 54.03 71.31 4.44 1675.67 33.43 Full (n=6) 43.55 1.46 --- --- 77.32 2.27 1442.07 54.03 66.23 4.44 1699.98 33.43 ANOVA F P F P F P F Pâ¡ F P F P Irrig (df=1) 0.91 0.384 --- --- 0.49 0.517 1.70 0.249 1.84 0.233 0.55 0.490 mulch (df=2) 0.69 0.512 3.88 0.057 0.55 0.585 0.26 0.775 1.23 0.312 1.12 0.345 Irrig*mulch (df=2) 0.66 0.527 --- --- 0.42 0.664 0.04 0.962 1.23 0.315 0.66 0.530 Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. Site - the three sites (Sites 1, 2, 3). ---' indicates no data of leaf area collected for irrigation at Site 1. â¡ =data was log-transformed for analysis 150 Table E2 Effects of mulches and postharvest deficit irrigation on the contents of key plant leaf macro-nutrients over two seasons and in 2016 and 2017 at Site 1. Mulches (2016-2017) Ca (mg kg-1) SE K (mg kg-1) SE Mg (mg kg-1) SE P (mg kg-1) SE N (%) SE C (%) SE S (mg kg-1) SE Bare (n=12) 1.35 0.07 2.52b 0.06 0.31a 0.01 0.24b 0.00 3.05 0.04 48.19 0.16 0.18 0.00 Compost (n=12) 1.26 0.07 2.73a 0.06 0.29b 0.01 0.26a 0.00 3.05 0.04 48.39 0.16 0.18 0.00 Woodchips(n=12) 1.34 0.07 2.55b 0.06 0.31a 0.01 0.25a 0.00 2.96 0.04 48.24 0.16 0.18 0.00 ANOVA F P F P F P F P F P F P F P mulch (df = 2) 1.56 0.232 7.93 0.003 5.25 0.014 5.87 0.009 1.58 0.253 1.93 0.168 0.05 0.952 Mulches (2016) Bare (n=12) 1.16 0.05 2.68 0.05 0.30 0.01 0.24 0.00 3.09 0.05 48.39 0.17 0.19 0.00 Compost (n=12) 1.14 0.05 2.78 0.05 0.30 0.01 0.24 0.00 3.11 0.05 48.60 0.17 0.19 0.00 Woodchips(n=12) 1.11 0.05 2.72 0.05 0.30 0.01 0.24 0.00 3.00 0.05 48.38 0.17 0.18 0.00 ANOVA F P F P F P F P F P F P F P mulch (df = 2) 0.42 0.671 0.95 0.418 0.67 0.534 1.14 0.359 1.42 0.286 0.65 0.545 0.81 0.473 Mulches (2017) Bare (n=12) 1.53 0.07 2.37 0.07 0.33a 0.01 0.24b 0.01 3.00 0.06 47.99 0.18 0.18 0.00 Compost (n=12) 1.38 0.07 2.67 0.07 0.29b 0.01 0.28a 0.01 2.99 0.06 48.29 0.20 0.18 0.00 Woodchips(n=12) 1.58 0.07 2.39 0.07 0.33a 0.01 0.26a 0.01 2.93 0.06 48.10 0.20 0.18 0.00 ANOVA F P F P F P F P F P F P F P mulch (df = 2) 2.27 0.154 9.56 0.005 9.78 0.004 8.29 0.008 0.48 0.635 0.62 0.562 0.36 0.708 These macro-nutrients include calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), nitrogen (N), carbon (C) and sulfur (S). Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. 151 Table E3 Effects of mulches and postharvest deficit irrigation (PDI) on the contents of key plant leaf micro-nutrients over two seasons and in 2016 and 2017 at Site 1. Mulches (2016-2017) Al (mg kg-1) SE B (mg kg-1) SE Cu(mg kg-1) SE Fe (mg kg-1) SE Mn (mg kg-1) SE Na (mg kg-1) SE Zn (mg kg-1) SE Bare (n=12) 61.99 10.20 65.71 4.22 7.05a 0.28 110.73 8.42 100.27b 5.01 40.04 2.59 51.30 7.72 Compost (n=12) 60.44 10.20 61.93 4.22 7.28a 0.28 110.90 8.42 91.56b 4.82 38.19 2.59 51.15 7.72 Woodchips(n=12) 61.34 10.20 64.49 4.22 6.10b 0.28 113.33 8.42 120.26a 4.91 35.95 2.59 56.33 7.72 ANOVA$ F P F P F P F P F Pâ¡ F P F P mulch (df = 2) 0.14 0.872 1.44 0.232 8.72 0.002 0.26 0.771 9.02 0.001 1.04 0.389 1.65 0.214 Mulches (2016) Bare (n=12) 29.60 4.00 55.78a 2.22 7.83a 0.34 86.59 2.69 93.49 5.46 38.28 4.13 74.10 5.05 Compost (n=12) 29.22 3.99 45.60b 2.22 7.73a 0.34 84.93 2.69 95.09 5.24 36.19 4.13 74.56 5.05 Woodchips(n=12) 28.25 3.99 53.24a 2.22 6.54b 0.34 87.50 2.69 107.43 5.24 31.90 4.13 82.70 5.05 ANOVA F P F P F P F P F P F P F P mulch (df = 2) 0.83 0.465 8.18 0.008 4.50 0.040 0.35 0.712 2.08 0.175 0.70 0.518 0.92 0.430 Mulches (2017) Bare (n=12) 93.35 5.74 75.64 2.77 6.27ab 0.31 134.87 6.87 107.26b 7.64 41.80 2.71 28.50 1.72 Compost (n=12) 91.66 5.74 78.26 2.77 6.82a 0.31 136.86 6.87 88.02b 7.31 40.19 2.71 27.74 1.72 Woodchips(n=12) 94.44 5.74 75.75 2.77 5.67b 0.31 139.16 6.87 138.86a 7.31 40.00 2.71 29.96 1.72 ANOVA F P F P F P F P F Pâ¡ F P F P mulch (df = 2) 0.11 0.895 0.66 0.540 5.28 0.027 0.16 0.853 12.93 0.002 0.34 0.721 0.69 0.525 These micro-nutrients include boron (B), copper (Cu), iron (Fe) and manganese (Mn)] and beneficial nutrients [aluminum (Al) and sodium (Na). Data expressed as mean and standard errors (SE). Organic amendments sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. 152 Table E4 Effects of mulches and postharvest deficit irrigation on the contents of key plant leaf macro-nutrients over two seasons and in 2016 and 2017 at Site 2. Mulches (2016-2017) Ca (mg kg-1) SE K (mg kg-1) SE Mg (mg kg-1) SE P (mg kg-1) SE TN (%) SE TC (%) SE S (mg kg-1) SE Bare (n=12) 1.86a 0.06 2.44b 0.07 0.35a 0.01 0.24a 0.01 2.92 0.05 47.43 0.22 0.18 0.00 Compost (n=12) 1.73b 0.06 2.66a 0.07 0.31b 0.01 0.22b 0.01 2.92 0.05 47.43 0.22 0.18 0.00 Woodchips(n=12) 1.88a 0.06 2.50b 0.07 0.36a 0.01 0.24a 0.01 2.84 0.05 47.12 0.22 0.17 0.00 ANOVA F P F P F P F P F P F P F P mulch (df = 2) 4.09 0.031 10.76 0.001 11.66 <0.001 7.11 0.004 2.22 0.132 2.25 0.129 1.94 0.168 Mulches (2016) Bare (n=12) 1.93 0.09 2.63b 0.05 0.33ab 0.01 0.22 0.01 2.89a 0.09 47.00 0.18 0.18 0.00 Compost (n=12) 1.82 0.09 2.82a 0.05 0.31b 0.01 0.22 0.01 2.92a 0.09 46.79 0.18 0.18 0.00 Woodchips(n=12) 1.95 0.09 2.69b 0.05 0.35a 0.01 0.23 0.01 2.78b 0.09 46.42 0.18 0.17 0.00 ANOVA F P F P F P F P F P F P F P mulch (df = 2) 1.08 0.377 3.40 0.075 5.42 0.025 0.41 0.677 3.30 0.080 2.79 0.109 1.85 0.207 Mulches (2017) Bare (n=12) 1.79a 0.07 2.25b 0.07 0.36a 0.01 0.26 0.01 2.95 0.05 47.86 0.22 0.18 0.00 Compost (n=12) 1.64b 0.07 2.51a 0.07 0.32b 0.01 0.24 0.01 2.92 0.05 48.08 0.22 0.18 0.00 Woodchips(n=12) 1.82a 0.07 2.32b 0.07 0.36b 0.01 0.26 0.01 2.90 0.05 47.83 0.22 0.17 0.00 Irrigation (Irrig) PDI (n=6) 1.77 0.06 2.32 0.06 0.35 0.01 0.26 0.01 2.95 0.04 48.02 0.20 0.18 0.00 Full (n=6 ) 1.72 0.06 2.40 0.06 0.35 0.01 0.25 0.01 2.90 0.04 47.82 0.20 0.17 0.00 ANOVA F P F Pâ¡ F P F P F P F P F P Irrig (df = 1) 0.64 0.459 2.04 0.212 0.15 0.712 2.45 0.179 1.05 0.353 1.00 0.363 0.20 0.677 mulch (df = 2) 3.47 0.051 6.68 0.006 9.12 0.002 1.19 0.324 0.33 0.724 0.80 0.464 0.56 0.581 Irrig*mulch(df=2) 0.18 0.833 0.31 0.737 0.48 0.626 1.08 0.359 1.93 0.172 0.11 0.895 0.23 0.794 These macro-nutrients include calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), nitrogen (N), carbon (C) and sulfur (S). Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. 153 Table E5 Effects of mulches and postharvest deficit irrigation on the contents of key plant leaf micro-nutrients over two seasons and in 2016 and 2017 at Site 2. Mulches (2016-2017) Al (mg kg-1) SE B (mg kg-1) SE Cu(mg kg-1) SE Fe (mg kg-1) SE Mn (mg kg-1) SE Na (mg kg-1) SE Zn (mg kg-1) SE Bare (n=12) 132.46 6.82 58.86 1.43 6.93 0.26 188.47 9.48 75.33b 2.75 54.1 3.52 34.14 3.73 Compost (n=12) 124.46 6.82 60.04 1.43 6.41 0.26 176.73 9.48 79.25ab 2.71 51.22 3.52 35.28 3.73 Woodchips(n=12) 125.97 6.82 59.68 1.43 6.33 0.26 182.57 9.48 83.35a 2.68 53.75 3.52 33.84 3.73 ANOVA F Pâ¡ F P F P F Pâ¡ F Pâ¡ F P F Pâ¡ mulch (df = 2) 0.42 0.662 0.27 0.763 1.89 0.175 0.38 0.686 4.17 0.029 0.2 0.822 0.27 0.762 Mulches (2016) Bare (n=12) 144.05 8.09 56.75 1.35 7.15 0.4 200.69 12.88 82.56 3.73 69.28 3.04 41.41 6.19 Compost (n=12) 133.74 8.09 57.29 1.35 6.62 0.4 187.23 12.88 88.04 3.73 64.63 3.04 41.59 6.19 Woodchips(n=12) 135.34 8.09 57.18 1.35 6.28 0.4 193.44 12.88 87.38 3.73 69.63 3.04 42.25 6.19 ANOVA F P F P F P F P F Pâ¡ F P F P mulch (df = 2) 0.47 0.637 0.05 0.947 1.98 0.188 0.27 0.766 0.79 0.482 1.15 0.357 0.03 0.975 Mulches (2017) Bare (n=12) 120.86 10.4 60.98 2.11 6.7 0.35 176.24 13.8 75.28 3.75 38.92 2.09 26.87b 1.16 Compost (n=12) 115.17 10.4 62.79 2.11 6.2 0.35 166.23 13.8 73.73 3.75 37.81 2.09 28.97a 1.16 Woodchips(n=12) 116.6 10.4 62.18 2.11 6.38 0.35 171.7 13.8 79.32 3.75 37.87 2.09 25.44b 1.16 Irrigation (Irrig) PDI (n=6) 114.59 11.92 60.72 2.16 6.61 0.33 171.22 15.07 75.07 3.39 36.94 2.39 25.91 1.23 Full (n=6) 120.5 11.92 63.25 2.16 6.25 0.33 171.56 15.07 77.15 3.39 39.46 2.39 28.27 1.23 ANOVA F Pâ¡ F P F P F Pâ¡ F Pâ¡ F P F Pâ¡* Irrig (df = 1) 0.13 0.734 0.69 0.445 0.61 0.469 0 0.988 0.19 0.681 0.7 0.441 1.85 0.232 mulch (df = 2) 0.3 0.744 0.26 0.771 0.63 0.541 0.22 0.806 0.67 0.524 0.28 0.759 3.62 0.046 Irrig*mulch (df =2) 2.43 0.114 0.26 0.777 1.17 0.331 1.99 0.163 0.34 0.715 0.48 0.628 1.66 0.216 These micro-nutrients include boron (B), copper (Cu), iron (Fe) and manganese (Mn)] and beneficial nutrients [aluminum (Al) and sodium (Na). Data showed means and standard error (SE) in each parameter. Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis. 154 Table E6 Effects of mulches and postharvest deficit irrigation on the contents of key plant leaf macro-nutrients over two seasons and in 2016 and 2017 at Site 3. Mulches (2016-2017) Ca (mg kg-1) SE K (mg kg-1) SE Mg (mg kg-1) SE P (mg kg-1) SE N (%) SE C (%) SE S (mg kg-1) SE Bare (n=12) 1.24a 0.04 2.06b 0.08 0.36a 0.02 0.21b 0.01 2.83b 0.04 48.77 0.21 0.17b 0.00 Compost (n=12) 1.12b 0.04 2.67a 0.08 0.29b 0.02 0.24a 0.01 2.97a 0.04 48.67 0.21 0.18a 0.00 Woodchips(n=12) 1.29a 0.04 2.13b 0.08 0.35a 0.02 0.23a 0.01 2.79b 0.04 48.79 0.21 0.17b 0.00 ANOVA F P F P F P F P F P F P F P mulch (df = 2) 11.67 <0.001 102.34 <0.001 26.21 <0.001 7.68 0.003 8.80 0.002 0.18 0.834 6.58 0.006 Mulches (2016) Bare (n=12) 1.13 0.04 2.28b 0.06 0.29a 0.01 0.20b 0.00 2.90b 0.06 49.14 0.25 0.17 0.00 Compost (n=12) 1.09 0.04 2.90a 0.06 0.24b 0.01 0.21a 0.00 3.03a 0.06 48.92 0.25 0.18 0.00 Woodchips(n=12) 1.21 0.04 2.31b 0.06 0.29a 0.01 0.21a 0.00 2.86b 0.06 49.46 0.25 0.17 0.00 ANOVA F P F P F P F P F P F P F P mulch (df = 2) 2.98 0.10 49.69 <0.001 6.31 0.02 3.59 0.067 3.22 0.083 1.44 0.283 4.99 0.032 Mulches (2017) Bare (n=12) 1.34a 0.04 1.85c 0.08 0.42a 0.01 0.23b 0.01 2.77b 0.04 48.41 0.23 0.16b 0.00 Compost (n=12) 1.14b 0.04 2.44a 0.08 0.33b 0.01 0.26a 0.01 2.91a 0.04 48.43 0.23 0.18a 0.00 Woodchips(n=12) 1.37a 0.04 1.96b 0.08 0.41a 0.01 0.25a 0.01 2.72b 0.04 48.12 0.23 0.17ab 0.00 Irrigation (Irrig) PDI (n=6) 1.28 0.05 2.07 0.08 0.38 0.01 0.24 0.01 2.80 0.05 48.31 0.22 0.17 0.00 Full (n=6) 1.29 0.05 2.10 0.08 0.40 0.01 0.26 0.01 2.80 0.05 48.33 0.22 0.17 0.00 ANOVA F P F P F P F P F P F P F P Irrig (df =1) 0.03 0.871 0.08 0.789 1.73 0.246 3.52 0.119 0.00 0.981 0.01 0.946 0.17 0.694 mulch (df =2) 16.86 <0.001 84.92 <0.001 31.71 <0.001 5.25 0.015 8.23 0.003 1.40 0.269 2.68 0.093 Irrig*mulch (df =2) 1.42 0.266 0.43 0.656 1.10 0.351 0.66 0.527 0.92 0.416 0.85 0.444 0.41 0.672 These macro-nutrients include calcium (Ca), potassium (K), magnesium (Mg), phosphorus (P), total nitrogen (N), total carbon (C) and sulfur (S). Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. 155 Table E7 Effects of mulches and postharvest deficit irrigation on the contents of key plant leaf micro-nutrients over two seasons and in 2016 and 2017 at Site 3. Mulches (2016-2017) Al (mg kg-1) SE B (mg kg-1) SE Cu(mg kg-1) SE Fe (mg kg-1) SE Mn (mg kg-1) SE Na (mg kg-1) SE Zn (mg kg-1) SE Bare (n=12) 98.71a 13.87 75.76 3.82 6.67 0.37 138.78 15.72 45.24b 1.95 36.19 4.13 13.15b 0.44 Compost (n=12) 87.43b 13.87 75.91 3.82 6.47 0.37 131.54 15.72 47.12b 1.95 32.46 4.13 14.32a 0.44 Woodchips(n=12) 97.48a 13.87 74.80 3.82 6.33 0.37 138.22 15.72 52.15a 1.95 34.15 4.13 13.45b 0.44 ANOVA F Pâ¡ F P F P F Pâ¡ F Pâ¡ F P F Pâ¡ mulch (df = 2) 5.57 0.011 0.15 0.859 0.85 0.442 0.22 0.802 5.89 0.009 0.89 0.442 3.12 0.064 Mulches (2016) Bare (n=12) 53.87 8.89 66.11 4.52 5.67a 0.27 86.08 2.55 46.14 2.73 39.93 6.66 12.52b 0.60 Compost (n=12) 51.36 8.89 65.77 4.52 5.88a 0.27 91.26 2.55 49.45 2.73 36.14 6.66 14.43a 0.60 Woodchips(n=12) 53.53 8.89 67.43 4.52 5.17b 0.27 85.19 2.55 50.85 2.73 36.42 6.66 13.26b 0.60 ANOVA F P F P F P F P F Pâ¡ F P F P mulch (df = 2) 0.99 0.41 0.15 0.87 3.39 0.08 1.97 0.19 1.20 0.34 0.80 0.48 8.43 0.007 Mulches (2017) Bare (n=12) 143.54a 8.18 85.42 3.25 7.66 0.40 191.48a 10.90 44.35b 2.86 32.46a 2.11 13.77 0.65 Compost (n=12) 123.50b 8.18 86.05 3.25 7.07 0.40 171.82b 10.90 44.79b 2.86 28.78b 2.11 14.22 0.65 Woodchips(n=12) 141.43a 8.18 82.17 3.25 7.49 0.40 191.25a 10.90 53.45a 2.86 31.87a 2.11 13.63 0.65 Irrigation treatment (Irrig) PDI (n=6) 132.68 7.97 84.31 3.18 7.15 0.40 178.56 10.57 48.07 2.82 30.39 2.26 13.12 0.55 Full (n=6) 139.62 7.97 84.78 3.18 7.67 0.40 191.14 10.57 46.98 2.82 31.68 2.26 14.62 0.55 ANOVA F Pâ¡ F P F P F Pâ¡ F Pâ¡ F P F Pâ¡ Irrig (df =1) 0.87 0.395 0.02 0.884 1.48 0.279 2.11 0.206 0.14 0.726 0.26 0.633 3.97 0.103 mulch (df =2) 4.69 0.021 1.03 0.377 1.34 0.285 3.29 0.058 7.37 0.004 2.77 0.087 0.24 0.785 Irrig*mulch (df =2) 2.21 0.136 0.98 0.394 0.36 0.705 2.43 0.113 0.08 0.921 0.42 0.660 0.18 0.833 These nutrients include boron (B), copper (Cu), iron (Fe) and manganese (Mn)] and beneficial nutrients [aluminum (Al) and sodium (Na)]. Data expressed as mean and standard errors (SE). Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 156 Appendix F: Effects of mulches and postharvest deficit irrigation on fruit yield and quality at each site The influence of organic amendments on fruit yield and quality of sweet cherry for one season (2017) at Site 1 and over two seasons (2016 - 2017) at Sites 2 and 3. The quality attributes include firmness, total soluble solids (SSC), pH, titratable acidity (TA), fruit color, stem pull force (SPF) and fruit yield. Data show means and standard error (SE) for each parameter. Sites Mulches Firmness (g/mm) SE SSC (%) SE pH SE TA SE Color SE SPF (kg) SE Yield (kg plot-1) SE Bare (n=12) 420.48 9.49 21.93 0.38 4.32 0.02 5.50 0.13 3.54 0.09 0.73b 0.04 13.79 2.07 Site 1 (2017) Compost (n=12) 448.49 9.49 21.65 0.38 4.38 0.02 5.24 0.13 3.54 0.09 0.86a 0.04 13.78 2.07 Woodchips (n=12) 429.59 9.49 21.23 0.38 4.32 0.02 5.09 0.13 3.47 0.09 0.72b 0.04 12.99 2.07 ANOVA F P F P F P F P F P F P F P mulch (df=2) 2.56 0.126 0.93 0.427 3.2 0.086 4.1 0.05 0.2 0.82 4.95 0.03 0.06 0.94 Bare (n=12) 466.62a 9.22 19.96 0.75 4.21 0.02 4.93 0.27 3.60 0.10 0.95 0.04 16.16 3.84 Site 2 (2016-17) Compost (n=12) 457.80ab 9.22 19.50 0.75 4.21 0.02 5.12 0.27 3.68 0.10 0.96 0.04 18.90 3.84 Woodchips (n=12) 451.64b 9.35 19.95 0.75 4.20 0.02 5.09 0.27 3.70 0.10 0.94 0.04 17.11 3.84 ANOVA F P F P F P F P F P F P F P mulch (df=2) 4.17 0.021 1.65 0.214 0.25 0.781 1.19 0.324 0.48 0.623 0.18 0.833 0.35 0.71 Bare (n=12) 386.10 26.57 20.91 0.34 4.13 0.03 5.92 0.21 3.97 0.15 0.65 0.07 5.80 1.37 Site 3 (2016-17) Compost (n=12) 390.70 26.57 20.53 0.34 4.20 0.03 6.17 0.21 3.92 0.15 0.75 0.07 5.65 1.39 Woodchips (n=12) 384.53 26.57 21.37 0.34 4.15 0.03 6.19 0.21 4.00 0.15 0.71 0.07 5.28 1.37 ANOVA F P F P F P F P F P F P F P mulch (df=2) 0.13 0.877 2.92 0.083 2.44 0.119 1.3 0.296 0.57 0.574 2.95 0.081 0.47 0.64 Mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. 157 Appendix G: Responses of soil chemical properties to soil amendment at Site 4 in every season. Table G1 Effects of fumigation, organic amendment and legacy effects mulch and P-fertigation on key soil nutrients in 2016 and 2017 season at Site 4. Treatment (2016) POXC (mg kg-1) SE C (%) SE N (%) SE P (mg kg-1) SE OM (%) SE C:N SE Fumigation (n=6): Fum 390.00 23.64 1.03 0.07 0.07 0.01 77.94 8.28 2.65 0.22 13.98 0.35 No Fum 440.00 23.64 1.13 0.07 0.08 0.01 67.45 8.28 2.88 0.22 14.55 0.35 Compost (n=12): Compost 480.00a 23.18 1.24a 0.07 0.09a 0.01 73.19 7.33 3.00a 0.22 14.62a 0.35 No Comp 350.00b 23.18 0.92b 0.07 0.07b 0.01 72.20 7.33 2.53b 0.22 13.92b 0.35 History (n=24): Mulch 468.00a 23.69 1.23a 0.07 0.08a 0.01 62.68b 7.47 3.02a 0.21 15.79a 0.35 No mulch/P 387.00b 23.69 0.99b 0.07 0.07b 0.01 64.27b 7.47 2.59b 0.21 13.58b 0.35 P-fert 390.00b 23.69 1.02b 0.07 0.075ab 0.01 91.12a 7.47 2.68b 0.21 13.43b 0.35 ANOVA F P F P F P F P F P F P fum (df=1) 4.46 0.0885 3.14 0.1368 0.44 0.5371 1.27 0.3103 2.02 0.2146 4.65 0.0836 comp (df=1) 35.62 <0.001 34.5 <0.001 14.09 0.004 0.04 0.8526 8.14 0.017 6.95 0.025 fum*comp (df=1) 1.02 0.3363 0.56 0.4704 0.19 0.6682 4.02 0.0728 0.04 0.8447 0.02 0.9026 his (df=2) 9.87 <0.001 21 <0.001 3.35 0.045 19.19 <0.001 13 <0.001 69.72 <0.001 fum*his (df=2) 1.7 0.1957 6.66 0.003 7.24 0.002 3.65 0.035 4.61 0.016 1.09 0.3476 comp*his (df=2) 0.69 0.5083 0.51 0.6058 1.58 0.2186 0.42 0.6615 0.02 0.9844 1.34 0.2739 fum*comp*his (df=2) 1.45 0.2474 2.63 0.0848 1.16 0.3228 1.9 0.1629 0.41 0.6658 4.18 0.023 (2017) Fumigation (n=6): Fum 407.97 25.32 1.13 0.08 0.08 0.01 60.66 6.41 2.99 0.31 13.54 0.28 No Fum 428.74 25.32 1.15 0.08 0.09 0.01 57.56 6.41 3.23 0.31 13.63 0.28 Compost (n=12): Compost 441.71a 22.37 1.28a 0.08 0.09a 0.01 58.71 5.98 3.32a 0.31 13.93a 0.28 No Comp 395.00b 22.37 1.01b 0.08 0.08b 0.01 59.51 5.98 2.90b 0.31 13.24b 0.28 158 Treatment (2016) POXC (mg kg-1) SE C (%) SE N (%) SE P (mg kg-1) SE OM (%) SE C:N SE History (n=24): Mulch 457.49a 24.56 1.34a 0.08 0.09a 0.01 52.07b 6.09 3.47a 0.30 14.98a 0.28 No mulch/P-fert 376.90b 24.56 1.03b 0.08 0.08b 0.01 51.31b 6.09 2.86b 0.30 12.88b 0.28 P-fert 420.67ab 24.56 1.06b 0.08 0.08b 0.01 73.95a 6.09 3.00b 0.30 12.89b 0.28 ANOVA F P F P F P F Pâ¡ F P* F P fum (df=1) 0.44 0.5343 0.16 0.703 0.68 0.448 1.02 0.360 0.75 0.426 0.17 0.694 comp (df=1) 5.33 0.044 25.6 0.001 10.82 0.008 0 0.998 5.65 0.039 9.15 0.013 fum*comp (df=1) 0.13 0.7252 0.37 0.557 0 1.000 2.93 0.118 0.11 0.747 0.02 0.888 his (df=2) 5.30 0.009 40.3 <0.001 7.55 0.002 19.81 <0.001 14.00 <0.001 62.44 <0.001 fum*his (df=2) 1.91 0.1617 2.68 0.081 3.19 0.052 3.67 0.035 1.34 0.273 4.09 0.024 comp*his (df=2) 2.66 0.0824 0.57 0.571 0.12 0.886 0.45 0.644 1.79 0.180 2.34 0.109 fum*comp*his (df=2) 1.96 0.1539 1.06 0.355 0.20 0.823 5.99 0.005 0.27 0.7646 5.20 0.010 The nutrients include POXC (permanganate oxidizable carbon), N (total nitrogen), C (total carbon), OM (organic matter), K (potassium) and Mg (magnesium). Data expressed as mean and standard errors (SE). Organic amendments sharing the same letter within a column do not differ significantly (at P â¤ 0.05 orP â¤ 0.1 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 159 Table G2 Effects of fumigation, organic amendment and legacy effects mulch and P-fertigation on different forms of C and N as well as C:N in 2016 and 2017 season at Site 4. Treatment (2016) Org C (mg kg-1) SE NO3 (%) SE NH4 (%) SE pH SE EC (uS cm-1) SE Fumigation (n=6): Fum 206.23 13.94 3.08 0.78 1.07 0.18 7.27 0.05 16.72a 2.06 No Fum 198.23 13.94 2.62 0.77 1.16 0.18 7.39 0.05 10.32b 2.06 Compost (n=12): Compost 237.93a 13.94 3.59 0.78 1.29a 0.18 7.35 0.05 13.70 2.06 No Comp 166.53b 13.94 2.11 0.77 0.95b 0.18 7.31 0.05 13.34 2.06 History (n=24): Mulch 227.46a 13.20 3.10 0.62 1.28 0.18 7.21b 0.05 14.96 1.90 No mulch/P-fert 191.00b 13.20 2.62 0.61 1.11 0.18 7.45a 0.05 13.22 1.90 P-fert 188.22b 13.20 2.82 0.61 0.96 0.18 7.34a 0.05 12.38 1.90 ANOVA F P F P F P F P F P fum (df=1) 0.11 0.7572 4.2 0.0958 0.38 0.5654 4.62 0.0844 8.48 0.033 comp (df=1) 26.18 <0.001 4.86 0.052 5.37 0.043 0.72 0.4154 0.36 0.5642 fum*comp (df=1) 0.02 0.9029 5.25 0.045 0.13 0.7264 4.14 0.0692 0 0.9637 his (df=2) 7.51 0.002 0.99 0.383 2.71 0.0786 13 <0.001 0.93 0.404 fum*his (df=2) 3.23 0.0501 1.75 0.1886 0.08 0.9206 1.57 0.2208 2.21 0.1225 comp*his (df=2) 0.26 0.7721 5.98 0.006 0.46 0.6359 1.87 0.1672 0.29 0.7485 fum*comp*his (df=2) 0.20 0.8218 6.62 0.004 2.49 0.0958 1.41 0.2568 0.22 0.8069 (2017) Fumigation (n=6): Fum 182.29 15.50 13.54b 3.73 0.95 0.20 6.95 0.05 21.93 2.33 No Fum 192.73 15.50 22.03a 3.73 0.96 0.20 6.95 0.05 21.37 2.37 Compost (n=12): Compost 213.96a 15.50 17.43 3.73 1.02 0.18 6.95 0.05 21.29 2.33 No Comp 161.06b 15.50 18.14 3.73 0.89 0.18 6.95 0.05 22.01 2.37 History (n=24): 160 Treatment (2016) Org C (mg kg-1) SE NO3 (%) SE NH4 (%) SE pH SE EC (uS cm-1) SE Mulch 212.27a 14.90 18.09 3.95 1.20 0.17 6.86b 0.04 21.33 2.86 No mulch/P-fert 171.05b 14.90 15.60 3.95 0.81 0.17 7.01a 0.04 20.01 2.86 P-fert 179.22b 14.90 19.67 3.95 0.84 0.17 6.98a 0.04 23.60 2.93 ANOVA F Pâ¡ F Pâ¡ F Pâ¡ F P F P fum (df=1) 0.86 0.395 6.97 0.046 0.09 0.776 0 0.9667 0.75 0.4271 comp (df=1) 22.25 0.001 0.7 0.423 3.1 0.109 0.01 0.913 0.01 0.9062 fum*comp (df=1) 0 0.953 2.35 0.156 0.27 0.614 0 0.9508 0.27 0.6144 his (df=2) 19.62 <0.001 0.02 0.980 1.44 0.250 6.42 0.004 0.26 0.7761 fum*his (df=2) 4.91 0.012 1.41 0.255 0.56 0.574 1.19 0.3134 0.32 0.7288 comp*his (df=2) 3.21 0.051 0.62 0.541 0.79 0.461 0.56 0.5769 0.33 0.7242 fum*comp*his (df=2) 0.18 0.837 1.93 0.159 0.10 0.905 1.61 0.2133 1.31 0.2816 Data expressed as mean and standard errors (SE). Organic amendments sharing the same letter within a column do not differ significantly (at P â¤ 0.05 significance level), according to Tukeyâs test. â¡ =data was log-transformed for analysis 161 Table G3 Effects of fumigation, organic amendment and legacy effects mulch and P-fertigation on soil pH, EC, CEC as well as Al, Ca, Mn and Na in 2016 and 2017 season at Site 4. Treatment (2016) Al (mg kg-1) SE Ca (mg kg-1) SE K (mg kg-1) SE Mg (mg kg-1) SE Mn (mg kg-1) SE Na (mg kg-1) SE CEC (meq 100 g-1) SE Fumigation (n=6): Fum 0.60 0.04 1391.30 100.77 205.21 21.32 164.72 13.83 4.62 0.41 53.96a 5.34 9.00 0.66 No Fum 0.56 0.04 1509.59 100.77 228.03 21.32 178.16 13.83 4.44 0.41 39.58b 5.34 9.72 0.66 Compost (n=12): Compost 0.56 0.04 1519.45 99.14 239.40a 21.32 187.75a 11.87 4.27 0.41 45.25 5.34 9.89a 0.64 No Comp 0.60 0.04 1381.45 99.14 193.84b 21.32 155.13b 11.87 4.79 0.41 48.30 5.34 8.83b 0.64 History (n=24): Mulch 0.60 0.04 1494.57 93.64 234.90a 20.01 177.98a 11.72 6.07a 0.39 48.50 5.09 9.69a 0.61 No mulch/P-fert 0.58 0.04 1413.33 93.64 202.97b 20.01 166.77b 11.72 3.37c 0.39 44.63 5.09 9.09b 0.61 P-fert 0.57 0.04 1443.44 93.64 212.01b 20.01 169.58ab 11.72 4.15b 0.39 47.19 5.09 9.30ab 0.61 ANOVA F P F P F P F P F P F P F P fum (df=1) 0.53 0.498 1.97 0.2197 1.52 0.2728 0.71 0.4379 0.17 0.6952 9.28 0.029 1.57 0.2651 comp (df=1) 0.92 0.3598 3.28 0.1002 6.05 0.034 20.15 0.001 1.46 0.2549 0.42 0.5324 5.26 0.045 fum*comp (df=1) 0.87 0.3734 0.00 0.9708 1.63 0.2302 2.36 0.1555 6.29 0.031 0.39 0.5439 0.01 0.9067 his (df=2) 0.30 0.7395 2.87 0.0683 5.67 0.007 2.34 0.1098 38.46 <0.001 0.87 0.425 4.72 0.015 fum*his (df=2) 4.94 0.012 5.92 0.006 1.66 0.2028 1.64 0.2065 1.26 0.2936 2.33 0.1103 6.71 0.003 comp*his (df=2) 1.23 0.3038 0.14 0.8734 2.29 0.1142 0.21 0.8144 1.92 0.1603 0.2 0.8234 0.12 0.8895 fum*comp*his (df=2) 1.07 0.3512 0.36 0.6998 0.81 0.4531 2.59 0.0879 1.60 0.214 0.2 0.8194 0.61 0.5495 (2017) Fumigation (n=6): Fum 0.55 0.03 1525.45 108.91 165.08 13.33 168.99 12.23 3.91a 0.26 30.41 3.37 9.59 0.69 No Fum 0.60 0.03 1551.59 108.98 183.70 13.33 184.04 12.23 3.46b 0.26 25.05 3.37 9.96 0.69 Compost (n=12): Compost 0.59 0.03 1601.34 105.60 184.15 13.33 191.51a 10.11 3.46b 0.26 27.48 3.14 10.28a 0.65 162 Treatment (2016) Al (mg kg-1) SE Ca (mg kg-1) SE K (mg kg-1) SE Mg (mg kg-1) SE Mn (mg kg-1) SE Na (mg kg-1) SE CEC (meq 100 g-1) SE No Comp 0.56 0.03 1475.71 105.52 164.63 13.33 161.52b 10.11 3.91a 0.26 27.98 3.14 9.27b 0.65 History (n=24): Mulch 0.60 0.03 1607.49a 100.34 184.95 11.62 182.29 9.86 4.94a 0.27 27.10 2.98 10.28a 0.63 No mulch/P 0.56 0.03 1478.76b 100.15 165.10 11.62 171.50 9.86 2.92b 0.27 26.97 2.98 9.36b 0.63 P-fert 0.57 0.03 1529.32b 100.15 173.12 11.62 175.75 9.86 3.19b 0.27 29.11 2.98 9.68b 0.63 ANOVA F P F P F P F P F P F P F P fum (df=1) 4.02 0.1012 0.08 0.7912 1.26 0.3125 0.91 0.385 11.31 0.02 1.48 0.2784 0.31 0.6032 comp (df=1) 1.07 0.3248 2.7 0.1316 1.39 0.2663 14.76 0.003 6.32 0.031 0.02 0.8976 5.54 0.04 fum*comp (df=1) 0.01 0.9407 0.21 0.6536 0.28 0.6095 1.42 0.2607 5.08 0.048 0.1 0.7614 0.00 0.9726 his (df=2) 1.8 0.1794 7.59 0.002 2.55 0.0906 1.92 0.1594 68.03 <0.001 0.46 0.634 10.14 <0.001 fum*his (df=2) 2.41 0.1034 4.58 0.016 5.38 0.009 0.78 0.4664 1.47 0.2415 0.88 0.4226 4.10 0.024 comp*his (df=2) 0.05 0.9523 1.02 0.3709 1.37 0.2657 0.17 0.8475 2.12 0.1334 0.16 0.8559 0.29 0.7525 fum*comp*his (df=2) 1.55 0.2263 0.93 0.4021 1.39 0.262 1.02 0.3687 1.95 0.1563 1.49 0.2374 0.11 0.8922 Data expressed as mean and standard errors (SE). Organic amendments sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. 163 Appendix H: Responses of soil biological properties to soil amendment at Site 4 in every season. Effects of fumigation, organic amendment and legacy effects mulch and P-fertigation on soil biological properties, such as arbuscular mycorrhiza fungi (AMF) root colonization, abundance of root and soil nematodes (Pratylenchus spp.), microbial activity (fluorescein diacetate â FDA assay) and abundance of total bacteria (16S) and fungi (18S) in 2016 and 2017 seasons at Site 4. Data expressed as mean and standard errors (SE). Treatment (2016) AMF (%) SE P. penetrans (1g-1dry root) SE P. penetrans (100g-1 soil) SE Total nematodes (100 g-1 soil) SE FDA (µg g-1) SE Log (16S copy # g-1 dry soil) SE Log (18S copy # g-1 dry soil) SE Fumigation (n=6) Fum 20.64 3.23 120.34b 66.66 97.03 31.07 384.00 77.92 1.79 0.39 8.64 0.07 6.75 0.24 NoFum 22.58 3.23 448.26a 66.66 93.03 31.07 517.00 77.92 2.65 0.39 8.52 0.07 6.80 0.24 Compost (n=12) Compost 20.25 3.23 161.17 66.66 92.22 30.35 434.00 68.25 2.42 0.37 8.57 0.07 6.69b 0.22 NoComp 22.97 3.23 407.43 66.66 97.83 30.35 467.00 68.25 2.02 0.37 8.58 0.07 6.85a 0.22 History (n=24) Mulch 23.75 3.42 231.88 70.98 92.71 28.91 441.50 68.53 2.23 0.38 8.64 0.07 6.79 0.23 Nomulch- 19.92 3.42 303.08 70.98 91.67 28.91 434.00 68.53 2.19 0.38 8.53 0.07 6.74 0.23 P-fert 21.17 3.42 317.95 70.98 100.71 28.91 476.00 68.53 2.24 0.38 8.56 0.07 6.79 0.23 ANOVA F P F P F P F P F P F P F P fum (df=1) 0.42 0.547 17.31 0.008 0.15 0.716 2.04 0.212 5.53 0.065 2.41 0.181 0.1 0.759 comp (df=1) 0.24 0.633 4.68 0.055 1.1 0.318 0.36 0.560 2.06 0.181 0.05 0.828 5.7 0.038 fum*comp (df=1) 2.22 0.167 0.99 0.344 1.45 0.256 3.49 0.091 3.66 0.084 2.9 0.119 1.55 0.242 his (df=2) 2 0.150 0.06 0.943 0.26 0.771 0.42 0.657 0.02 0.982 2.42 0.102 0.23 0.798 fum*his (df=2) 2.1 0.136 1.32 0.281 1.67 0.201 0.4 0.672 1.88 0.166 1.76 0.186 0.21 0.809 comp*his (df=2) 0.19 0.826 0.19 0.824 0.95 0.395 1.3 0.284 0.62 0.544 2.2 0.124 0.44 0.645 fum*comp*his (df=2) 4.02 0.026 2.7 0.083 0.87 0.428 0.85 0.434 0.64 0.531 1.14 0.331 3.31 0.047 2017 Fumigation (n=6) Fum 31.29 2.55 211.49 49.47 90.01 21.32 758.33 94.69 3.31 0.48 8.80 0.05 7.26 0.06 164 Treatment (2016) AMF (%) SE P. penetrans (1g-1dry root) SE P. penetrans (100g-1 soil) SE Total nematodes (100 g-1 soil) SE FDA (µg g-1) SE Log (16S copy # g-1 dry soil) SE Log (18S copy # g-1 dry soil) SE NoFum 22.76 2.55 95.22 49.47 49.75 21.32 715.00 94.69 1.70 0.48 8.71 0.05 7.37 0.06 Compost (n=12) Compost 25.90 2.22 103.12 47.61 62.71 21.32 675.67 94.69 2.35 0.42 8.78 0.04 7.33 0.06 NoComp 28.16 2.22 203.60 47.61 77.05 21.32 797.67 94.69 2.65 0.42 8.73 0.04 7.30 0.06 History (n=24) Mulch 26.11 2.58 176.75 56.10 55.00 18.58 707.00 90.27 2.75 0.38 8.83a 0.05 7.36 0.06 Nomulch- 28.57 2.58 135.64 56.10 73.58 18.58 686.50 90.27 2.44 0.38 8.69b 0.05 7.26 0.06 P-fert 26.40 2.58 147.69 56.10 81.07 18.58 816.50 90.27 2.33 0.38 8.75ab 0.05 7.33 0.06 ANOVA F P F P F P F P F P F P F P fum (df=1) 5.61 0.064 0.57 0.485 1.39 0.291 0.15 0.719 5.40 0.068 2.79 0.156 3.47 0.122 comp (df=1) 0.75 0.406 2.24 0.165 0.85 0.377 1.15 0.309 0.23 0.640 2.35 0.157 0.49 0.502 fum*comp (df=1) 0.03 0.875 0.9 0.364 0.4 0.540 0.03 0.864 0.04 0.851 3.920 0.076 0.340 0.574 his (df=2) 0.35 0.704 0.19 0.832 0.99 0.380 1.35 0.272 1.57 0.222 6.980 0.003 2.950 0.064 fum*his (df=2) 1.87 0.168 0.08 0.922 0.43 0.654 0.73 0.489 4.91 0.013 0.230 0.793 1.620 0.210 comp*his (df=2) 0.81 0.453 1.12 0.337 0.1 0.908 0.25 0.778 0.13 0.878 0.070 0.929 4.870 0.013 fum*comp*his (df=2) 0.86 0.433 1.56 0.225 2.47 0.098 0.99 0.382 0.77 0.468 0.110 0.896 0.070 0.937 Organic amendments sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs test. 165 Appendix I. Interaction effects of irrigation and mulch Table I1 An interaction effect of postharvest deficit irrigation and mulches on total soil nematodes at Site 1, stem water potential at Site 2 and Al and TCSA over two sites (Site 2 and 3). Site1 Site 2 Site 2 & 3 Site 2 & 3 Irrig*mulch Total soil nematode(50g-1 soil) SE á´ªStem (MPa) SE Al (mg kg-1) SE TCSA (cm2) SE PDI Bare 152.00a 30.82 -1.01b 0.05 0.65b 0.09 64.39a 3.23 PDI Compost 132.00b 30.82 -1.01b 0.05 0.60b 0.10 68.16a 3.23 PDI Woodchips 116.00b 30.82 -1.04b 0.05 0.86a 0.10 66.93a 3.23 Full Bare 56.00b 30.82 -0.99b 0.05 0.62b 0.10 64.54ab 3.23 Full Compost 204a 30.82 -1.00b 0.05 0.62b 0.09 61.75b 3.23 Full Woodchips 104a 30.82 -0.93a 0.05 0.54b 0.09 66.69a 3.23 Interaction of irrigation (Irrig) and mulches sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according toTukeyâs LSD. Table I2 Interaction effect of fumigation and history on Total N, C, organic matter (OM) and organic C at Site 4. Fum*History Total N SE Total C SE OM SE Org C SE Fum Mulch 0.08a 0.01 1.23b 0.06 3.10ab 0.18 212.20a 11.6 Fum No-mulch/ no-P 0.08a 0.01 1.05cd 0.06 2.74c 0.18 195.00b 11.6 Fum P-fert 0.07b 0.01 0.97d 0.06 2.62c 0.18 175.60bc 11.6 NoFum Mulch 0.09a 0.01 1.35a 0.06 3.35a 0.18 227.60a 11.6 NoFum No-mulch/ no-P 0.07b 0.01 0.97d 0.06 2.70c 0.18 167.10c 11.6 NoFum P-fert 0.08a 0.01 1.12cd 0.06 3.02b 0.18 191.82b 11.6 Interaction of organic amendments (OA) sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs LSD. Table I3 An interaction effect of fumigation and history on Ca, Al, CEC and EC at Site 4. Fum*History Ca SE Al SE CEC SE EC SE Fum Mulch 1512.50b 77.1 0.65a 0.03 9.63ab 0.48 19.00a 2.15 Fum No-mulch/ no-P 1465.30bc 77.1 0.56b 0.03 9.32ab 0.48 19.19a 2.16 Fum P-fert 1397.32c 77.1 0.55b 0.03 8.93c 0.48 15.84ab 2.16 NoFum Mulch 1615.94a 77.1 0.59b 0.03 10.34a 0.48 16.18a 2.12 NoFum No-mulch/ no-P 1426.78bc 77.1 0.58b 0.03 9.13bc 0.48 14.61b 2.12 NoFum P-fert 1575.44ab 77.1 0.60ab 0.03 10.04a 0.48 16.87a 2.15 Interaction of organic amendments (OA) sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs LSD. 166 Table I4 An interaction effect of fumigation and compost on root P. penetrans, Mg and Mn at Site 4. Fum*Compost P.penetrans (1g-1 dry root) SE Mg (mg Kg-1 ) SE Mn (mg Kg-1 ) SE Fum Compost 121.21b 65.50 177.39b 9.54 3.67b 0.31 Fum No-Comp 210.62b 65.50 156.32c 9.54 4.85a 0.31 NoFum Compost 143.08b 65.50 201.87a 9.54 4.06b 0.31 NoFum No-Comp 400.41a 65.50 160.33bc 9.54 3.90b 0.31 Interaction of organic amendments (OA) sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according toTukeyâs LSD. Table I5 An interaction effect of fumigation, compost and history on total fungal abundance at Site 4. Fum*Comp*History Log (18S copy #g-1 dry soil) SE Fum Compost Mulch 7.03b 0.16 Fum Compost No-mulch/no-P 6.92b 0.16 Fum Compost P-fert 7.01b 0.16 Fum No-Comp Mulch 7.02b 0.16 Fum No-Comp No-mulch/no-P 7.06ab 0.16 Fum No-Comp P-fert 6.97b 0.16 NoFum Compost Mulch 7.01b 0.16 NoFum Compost No-mulch/no-P 7.02b 0.16 NoFum Compost P-fert 7.10ab 0.16 NoFum No-Comp Mulch 7.24a 0.16 NoFum No-Comp No-mulch/no-P 7.00b 0.16 NoFum No-Comp P-fert 7.16ab 0.16 Interaction of organic amendments (OA) sharing the same letter within a column do not differ significantly (at P â¤ 0.05 or P â¤ 0.1 significance level), according to Tukeyâs LSD.
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Impact of postharvest deficit irrigation and organic amendments on sweet cherry production at newly and previously established orchards in the Okanagan Valley
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