Final report for SW20-910
Project Information
Colorado peaches (Prunus persica) are an important specialty crop with excellent market acceptance and superior quality due to the unique growing conditions. Orchard replant disease (RD) is a soil-borne disease that affects young trees planted in sites where the same or closely related species were previously grown. RD has become a major production problem of the Colorado and Intermountain peach industry primarily due to the limited amount of land suitable to grow tree fruit crops. Annual losses in yield and orchard longevity due to RD are estimated to reach as much as 20%. Factors implicated in RD etiology in other crops include mainly soil pathogens (bacteria, fungi, oomycetes, nematodes) and phytotoxicity from allelopathic toxins in plant roots. However, in peach the biological component that causes RD remains largely unknown. Conventional management strategies have relied on broad-spectrum fumigants such as methyl bromide, which was phased-out due to environmental concerns. The lack of effective alternatives to control RD in conventional and organic stone fruit production systems, create an urgent need for environmentally sound and sustainable RD-management solutions. Herein a research-extension-producer team approach is implemented to understand peach RD etiology, evaluate sustainable alternative strategies to manage this disease and impact farmer decision-making by sharing results and end products through multiple methods. Alternative strategies tested in greenhouse, agricultural experimental station and on-farm in commercial orchards include cover crops, short-term crop rotation and use of RD-tolerant peach rootstocks to mitigate RD symptoms in commercial orchard systems. Our overall goal was to provide peach growers located in challenging climates with a bio-intensive orchard RD management strategy. A combined physiological and molecular approach sheds some light on the etiology of peach RD development. High-throughput molecular techniques combined with physiological measurements identified pathogenic microbiomes related with RD symptoms on susceptible rootstocks; which can allow for effective management decisions from a biological stand point. Our approach is also expected to generate information on the role of many other beneficial microbiomes that can mitigate RD stress and promote tree growth. Through this bottom-up research and extension/outreach approach we generated solid information on RD etiology and sustainable management strategies for peach production systems to improve soil health, and increase orchard productivity, longevity and grower profitability.
This project brings together university and USDA researchers, extension personnel and producers from three states (CO, UT, ID) together to develop best management solutions for RD in organic and conventional peach production systems under western Intermountain climatic conditions. The implementation of this research and education program provides reliable RD control tools and a clearer understanding of the etiology of this disease to the stakeholders and scientific community.
The goal of this project is to help improve the economic and environmental aspects of fruit production in Colorado and other western regions through sustainable and readily applicable peach orchard replant management solutions.
Our specific objectives (click: Graphical abstract) are to:
- Evaluate the influence cover and rotation crops on soil health and RD-mitigation on susceptible clonal peach seedlings in greenhouse conditions and on-farm
- Identify RD-tolerant rootstocks and the soil microbiome changes associated with RD development/resistance in greenhouse and on-farm conditions
- Impact farmer decision-making by sharing results through multiple methods
A timeline, in the form of a Gantt chart, for accomplishing each objective and identifying the major activities/milestones performance, duration and completion times is provided (click here: Gantt chart).
Cooperators
- - Producer
- (Researcher)
- (Educator and Researcher)
- - Technical Advisor - Producer
- - Producer
Research
Replant disease is a global challenge induced upon intensive cultivation of perennial crops (Zhu et al., 2016). After replanting of an orchard, one common symptom of replant disease is poor root health and reduction in overall plant health (Mazzola et al., 2012). Although the specific etiology is not fully understood, it is known that the reduced crop productivity due to replant disease is caused by a complex of parasites/pathogens, microbial plant competitors and repeated plantings of closely related plant species (Mazzola et al., 2012). Monoculture reduces crop production upon repeated plantings (Mazzola et al., 2012 Zhu et al., 2016). This reduced crop production can also be caused by abiotic factors like decreased soil fertility, poor soil structure, and residual herbicide activity (Mazzola et al., 2012). However, there is more support for a biological component since replant disease can persist in fallowed soils for many years which is atypical for toxins (Mazzola et al., 2012). Further support of a biotic component was provided by soil pasteurization studies where trees grown in fumigated soils grew better than trees grown in non-fumigated soils (Mazzola et al., 2012). Thus, it seems that the soil and rhizosphere microbiome is directly linked to replant disease.
Microbes within the soil microbiome are highly connected, and disturbances of different kinds can affect microbial growth and functionalities (Smith et al., 2016). Soil disruption can be caused by over fertilization, tillage, crop rotation, cover crop practices, solarization and chemical fumigation (Li et al., 2019). Previous disease management of replant disease involved chemical fumigation of orchard soils before the planting of saplings (Zhu et al., 2016). In general, soil fumigation appears to be limited in its ability to suppress re-plant disease since its benefit of inducing a growth response in an orchard only lasts a year and the disease rebounds completely within two growing seasons (Wang et al., 2019). Furthermore, fumigation is no longer a viable option due to environmental restrictions placed on the fumigants (Zhu et al., 2016). Thus, other more sustainable strategies crop rotations and cover crops could be added as a part of the solution in dealing with orchard replant disease.
Known benefits of crop rotation are pest management, fertility, and crop yield (Peralta et al., 2018). Additionally, temporal crop biodiversity may encourage beneficial ecosystem functions such as pest control, carbon sequestration, and nutrient cycling. It was also found that for no crop (fallow) treatments, disease suppressive potential was greatly diminished compared to the more diverse crop rotation treatments (Peralta et al., 2018). However, neither crop rotation or fallowing methods are practical against orchard replant disease (Zhu et al., 2016). Even though the value of crop rotation is plain to both organic and conventional agriculturists, this tool alone cannot solve the issue of replant disease and is only a part of the solution.
Cover cropping is a sustainable agricultural technique defined as planting crops for a purpose other than harvest (MacLaren et al., 2019). Cover cropping can be used for soil preservation, nutrient restoration, and organic fertilizer through reincorporation (Altieri et al., 2015). Since cover crops affect the chemical and physical properties of the soil, the biological properties of the rhizosphere also change (Peralta et al., 2018). The rhizosphere is the narrow region of soil where plant-microbial symbioses occur between the soil microbiomes (which contains a multitude of bacteria and fungi) and the plant’s roots (Hao e al., 2016). Plant-microbial symbioses within the rhizosphere can potentially improve soil fertility and degrade toxic nutrients (Hrynkiewicz, K., and Baum, C. 2012). Furthermore, symbioses in the rhizosphere can influence pathogen populations (Peralta et al., 2018).
Use of replant disease-tolerant rootstocks is a valuable alternative to soil fumigation for apples. However, research on peach rootstocks is ongoing and has yet to provide effective replant disease-tolerance without affecting tree vigor, acclimation/cold hardiness and fruit quality (Minas et al., 2018). Excessive tree vigor that promotes heavier pruning practices or cold damage susceptibility can provide more ports of entry for tree canker pathogens. Thus, for a resilient conventional and organic replant disease control solution in challenging climates, rootstock evaluation is critical to be done locally to ensure that replant disease-tolerant rootstocks are adapted to the environment.
In this study, we planted corn, tomato, alfalfa, and fescue to be used as cover crops in disrupted and non-disrupted soils. We disrupted the soil using a steam autoclave for three cycles of 40 minutes. After 12 weeks we reincorporated the cover crops and planted replant disease susceptible peaches. In addition to the cover crops a list of Prunus app. rootstock genotypes of variable vigor where tested under greenhouse conditions in disrupted and non-disrupted soils.
Objective 1: Evaluate the influence cover and rotation crops on soil health and RD-mitigation on susceptible clonal peach seedlings in greenhouse conditions and on-farm
Effect of Soil Disruption on Cover Crop Growth in Greenhouse
Soil Sampling Site and Disinfection
RS soil for the experiment was acquired from a peach orchard research block, which was established in 2007 at the Colorado State University’s experimental orchard at the Western Colorado Research Center in Orchard Mesa, CO. This peach orchard was established using Prunus persica (peach) ‘Cresthaven’ scions with grafted peach ‘Lovell’ rootstocks. The soils from this area have been described as Billings silty clay loam (calcareous, mesic Typic Torrifluvents). RS soils were transported to Colorado State University’s Horticultural Center.
In the Horticultural Center, soils were passed through a metal sieve (2 cm wide) and homogenized. Samples of the replant soil were collected before and after autoclaving and stored at −80 °C to be used as controls for soil bacteriome analysis. Soil was placed in autoclave bags and then in a STERIS brand steam autoclave set on the 40 min liquid cycle at 121 °C, which was run three times. In between cycles, bags carrying the soil were shaken to redistribute the soil before being returned to the autoclave for a second and third time.
Then, 4 L black plastic pots (n = 100) were lined with Vigoro weed control fabric medium duty, placed on Vigoro 15.24 cm plastic plant saucers, and filled with c. 2.1 kg of either untreated RS soil (n = 50) or autoclaved RS soil (n = 50).
Seed Sterilization and Density for Cover Crops
Four crops were selected for this study: natural sweet F1 OG hybrid bicolor Sh2 corn (Zea mays), hybrid cherry tomato SUN gold F1 (Solanum lycopersicum), ranger alfalfa (Medicago sativa), and a fine fescue species mixture of chewing fescue (Festuca rubra ssp. Commutate), hard fescue (Festuca longifolia), and creeping red fescue (Festuca rubra). Alfalfa and fescue were selected since these cover crops are known to successfully establish in Colorado soils (Braun et al., 2020; Ervin & Koski, 1998). Tomato and corn have been shown to induce microbial shifts in RS soils under autoclaved conditions, which could potentially create a beneficial microbiome for the incoming peach crop cycle (Li et al., 2019). All cover crops were considered genetically distant from peaches. Seed densities were calculated by using recommended crop seed count or weight per square meter and adjusted by the 0.0222 m2 surface area of a 4 L pot. Corn and tomato treatments had one plant per pot (Ara et al., 2007; Ilker, 2011). For fescue, the recommended use of 50 lbs of seed per acre for high elevation soil in the western United States was used to calculate 0.54 g of fescue seeds per pot (Laycock, 1982). For alfalfa, the recommended use of 75 seeds of alfalfa per square foot was used to calculate 0.038 g of alfalfa seeds per pot (Rankin, 2008). For seed sterilization, 15 mL falcon tubes with seeds were filled with 3% NaOCl and vortexed at max speed (setting 10: 600–2700 RPM) for one minute. NaOCl was removed and seeds were then rinsed with autoclaved distilled water and vortexed at max speed for one minute, with this rinse step being repeated 5 times. Seeds were immediately planted into the soil. Each crop treatment had 10 replicates for autoclaved and non-autoclaved soils. Pots with only autoclaved and non-autoclaved soils served as a no-plant control and were watered to water-holding capacity daily.
Establishing the Cover Crops in a Greenhouse
The experiment was factorial with two factors: soil disinfection (2 levels: autoclave and non-autoclaved) and cover crops (5 levels: corn, tomato, alfalfa, fescue, and a no-plant control). Pots (n = 100) were set in a completely randomized design (5 × 20) using an online random block design generator (https://www.randomizer.org accessed on 26 February 2021) with one treatment per row. Pots were watered at water-holding capacity (c. 200 mL) for six days per week for 12 weeks. After 12 weeks, bulk soil samples were collected with a hand-sized soil probe either in the center of the pot or 2 cm from the base of the plant at a depth of 7 cm. Above ground crop biomass was cut into <2 cm pieces using scissors that were washed in 3% NaOCl followed by heat sterilization using a Bacti-Cinerator III™ from Monoject (St. Louis, MO, USA) in between samples. Above ground fresh biomass of cover crops was recorded, immediately incorporated into the soil within the first 3 cm of the same pot in which the crops had been grown, and left to decompose. After two weeks, bulk soil samples were collected.
Continued Greenhouse Experiment with Peaches
‘Lovell’ (Prunus persica) rootstock cultivar was grown from seeds in liners using pro-mix potting media in a greenhouse for 28 days. This RS-susceptible ‘Lovell’ was selected since this rootstock cultivar was grown in the orchard where the RS soil was collected. These four-week-old peach seedlings were transplanted into the pots that previously had cover crops and no-cover-crop controls. Peach seedlings were watered daily with c. 150 mL of tap water. Weeds and cover crops were continuously removed, and no fertilizer was added. Peaches grew for 22 weeks. For microbial analysis, bulk soil and rhizosphere soil was collected. Using a soil probe, bulk soil samples were collected from the top 7 cm of soil within 2 cm of the base of the tree trunk and immediately stored at −20 °C. Rhizosphere was defined as the soil adhering to the roots after the removal of bulk soil and gently shaking the root system. Rhizosphere soil was taken from light colored roots, placed into 15 mL falcon tubes, and immediately stored at −20 °C. Remaining soil on root systems were removed with tap water. Biomass was separated as either above- or below-ground and its weight was recorded. Fresh biomass samples were oven-dried at 90 °C for 72 h and were weighed for above- and below-ground dry biomass. Greenhouse experiments ran between 20 June and 1 August, humidity set point was 70%, cool set point was 24–26.5 °C, heat set point was 18–21 °C, relative humidity ranged from 21–80% (average = 55.5%), and actual temperature ranged from 18.9–38.3 °C (average = 25.4 °C).
Soil Analysis
Soil analysis (total nutrient digest and Haney H2O extract) was performed by WARD Laboratories, Inc. (Kearney, NE, USA) (Yost et al., 2018) on three bulk soil samples per treatment. Total nutrient digest analysis quantified the total values of elements in a soil (C, N, P, K, Ca, S, Mg, B, Zn, Mn, Fe, Cu, Mo). The Haney test uses different extracts from traditional soil test labs, and the extract analysis quantifies nutrients within the soil that are available to soil microorganisms by measuring soil respiration, water-soluble organic carbon, and nitrogen. Soil analyses of total nutrient digest and Haney H2O extract by soil treatment of autoclave, cover crop, and control treatments can be found in Table 1.
DNA Extraction
Total genomic DNA (gDNA) was extracted from 0.25 g of bulk and peach rhizosphere soil in a QIAcube instrument (Qiagen, Germantown, MD, USA) using PowerSoil® DNA kits by Qiagen. All DNA extractions were performed according to Qiagen’s instructions with a final elution volume of 100 μL. DNA concentration was quantified using a Qubit with broad range assay solutions. Of the ten replicates used for biomass, a subset of five replicates were used for bacterial DNA microbial analysis. Bulk soil samples were taken after cover crops were grown for 12 weeks, after cover crops had been incorporated for two weeks, and after peach trees had been growing for 22 weeks. The controls used were pre-extracted Zymo gDNA (Zymo Research Corporation, Irvine, CA, USA) (n = 4), HPLC water (n = 3), stock soil (n = 4), non-autoclaved soil (n = 5), and autoclaved soil (n = 4). In total, 220 samples were extracted.
Oxford Nanopore Library Preparation, Sequencing, and Bioinformatics Pipeline
Based on Qubit concentrations (ng/μL), extracted DNA was diluted 10× with HPLC water to lower DNA concentrations and minimize potential PCR inhibitors. Mastermix consisted of 10 μL Phusion HSII master mix, 7.2 μL H2O, 0.4 μL forward primer, and 0.4 μL reverse primer for a total of 18 μL Mastermix per 2 μL sample. Bacterial primers used were Bact_27F-Mn (5′-TTTCTGTTGGTGCTGATATTGCAGRGTTYGATYMTGGCTCAG-3′) and Bact_1492R-Mn (5′-ACTTGCCTGTCGCTCTATCTTC TACCTTGTTACGACTT-3′). Polymerase chain reaction (PCR) settings were 98 °C for 30 s, 98 °C for 15 s, 50 °C for 15 s, and 72 °C for 60 s for 25 cycles, and 72 °C for 5 min. After PCR, equal volumes of DNA and beads were mixed. A 96-pronged magnetic stand moved beads with adhering DNA into two 30 s rinses of 70% EtOH. DNA was eluted in a 96-well plate with 40 µL PCR grade water and beads were removed using a magnetic stand. DNA was quantified using a Qubit with high sensitivity assay solutions. The second PCR settings were 98 °C for 30 s, 98 °C for 15 s, 62 °C for 15 s, and 72 °C for 60 s for 25 cycles, and 72 °C for 5 min.
After a second PCR, DNA and barcodes were pooled in AMPure bead solution in a 96-well plate. Wells with suspended DNA and barcodes were pooled into a clean Lo-Bind tube. MinION sequencer was loaded with a flow cell (R9.4.1) and was prepared for DNA loading. To prepare the flow cell, air (c. 20 µL) was removed using a pipette. The flow cell was then primed with flush buffer, and pooled DNA was loaded into the sampling port. MinKNOW software (v23.04.5) was used to sequence the pooled library for 48 h. Raw data were downloaded and converted into fastq file format using Guppy_basecaller (v6.0.1). Barcodes were sorted by de-multiplex using Guppy_barcoder using barcode kit EXP-PBC096, trimmed, and reads were then filtered by quality and length (Filtlong minimum length: 1000 and mean quality: 70) (Cutadapt: -m 1000 -M 2000). Chimeras were identified and removed by Vsearch. Bacterial taxa were identified using EMU NCBI Reference Database. EMU error correction removed identified bacterial taxa based on alignment and abundance profiles. Bacterial taxa with <one per 10,000 reads were removed. Sequencing data came from three separate sequence runs, which were pooled for data analysis.
Statistical Analysis
All statistical analyses were performed in RStudio Version 1.4.1103. A non-parametric test, the Kruskal–Wallis rank sum test, was used to analyze fresh cover crop biomass and peach dry biomass by soil treatment (autoclaved vs. non-autoclaved). Pairwise comparisons using the Wilcoxon rank sum exact test was used to infer differences between plant biomass and soil treatment. For regressions analyzing soil nutrients from the end of the peach experiment, peach dry biomass was used. The Lagrange multiplier test was used for the regressions fit with broom and tidyverse packages in RStudio. The vegan package was used to test for significant differences between treatments with perMANOVA and visualized with a constrained principal coordinate analysis (PCoA). Bray–Cutis was used to determine distance for PCoAs. Homogeneity of multivariate dispersions was measured using betadisper from the vegan package. Differential abundance analysis was based on bacterial species counts that were transformed using a log2 fold-change and the Benjamini and Hochberg statistical method using the false discovery rate function (FDR) and 0.05 as the accepted threshold for the adjusted p-value.
Soil microbiome composition under increasing cover crop densities
Soil disinfection and cover crop seed density
Soil was collected from Colorado State University’s Agricultural Research, Development and Education Center South. Large debris were removed from the soil using metal sieves (2 cm wide). Autoclaved soil was used to reduce soil microbial biomass and community complexity and to maximize the impact the plant had on the soil microbiome (DiLegge et al., 2022; Monohon et al., 2021; Li et al., 2019) Soil was homogenized and then autoclaved in batches of approximately 13.5 kg in 61 × 76 cm polyethylene autoclave bags using a STERIS steam autoclave (Mentor, Ohio, USA) for three 40 min liquid cycles at 121 °C. After soils were autoclaved, they were pooled to reduce any potential variability associated with each autoclave cycle. Different seed density maximums were tested prior to the experiment showing 1–3, 24, and 48 plant densities had high seedling survivability, and senescence started at week four.
Cover crop greenhouse experiment
Plants were grown for 32 days from August 1 to September 1, 2021, in Colorado State University's Horticultural Center Greenhouse Facility. A microcosm was its own “pot” (6 × 4.9 × 5.6 cm) taken from a 36-cell tray, and each microcosm was separated by ~ 2 cm (Fig. 6). Pots were lined with a double layer of Vigoro Weed Control Fabric Medium Duty to reduce soil runoff. There were 7 diversity treatments (1. alfalfa, 2. brassica, 3. fescue, 4. alfalfa-brassica, 5. alfalfa-fescue, 6. brassica-fescue, 7. alfalfa-brassica-fescue) and 3 density treatments (low: 1–3 plants, medium: 24 plants, and high: 48 plants) for a total of 21 treatments (Table 4). Each treatment had 12 replicates for a total of 252 pots. Random block design of 21 × 12 was configured by an online random block design generator (https://www.randomizer.org). There was one treatment type per column. The reference control for this plant-plant competition/facilitation study was a single cover crop species to exemplify a plant with no competition/facilitation. Cover crop seed mixes and densities were manually counted and placed into microcentrifuge tubes. Each microcentrifuge tube was briefly vortexed to mix the seeds. Seeds were spread evenly into the pots with autoclaved soil using tweezers, which were washed with ethyl alcohol in between samples. To remedy seed germination failure, pregerminated seeds were planted into each pot 7 days into the experiment to reach the target densities. Plants were watered daily at water holding capacity with DI water to reduce the introduction of microbes and other contaminants. Additionally, DI water was used to mimic uncontaminated rainwater since cover crops are ideally not irrigated. As an aside, seeds were not sterilized as to prevent additional seed death and to maintain fundamental microbes on the surface of the seed coat for the respective plant. At the end of the experiment, the number of plants in each pot were counted.
Bulk soil collection
Bulk soil samples were collected at the end of the study, and prior to biomass harvest. Bulk soil refers to the surrounding soil, which has been influenced by an organism such as a plant but excludes the soil adhering to the roots which is known as the rhizosphere (Blouin & Jacquiod, 2020). Within each treatment, the top five replicates that best represented target densities were selected for bacteriome analysis. A core borer (1.5 cm diameter) was used to collect the surrounding bulk soil from the center of the pot without disturbing the above-ground biomass. The soil probe was sterilized between samples. Visible soil debris was scrubbed off the soil probe using a brush soaked in a tap water-Alconox (White Plains, New York, USA) solution. Next, the soil probe was rinsed with 2% bleach followed by 70% ethyl alcohol. Bulk soil cores were placed in a 15 ml falcon tube and immediately stored at − 20 °C. Bulk soil samples were taken over four days.
Plant biomass
Above ground biomass was measured for every sample (n = 235) and was harvested using scissors, which were surface sterilized between samples using a Bacti-Cinerator III (Monoject Scientific, St. Louis, Missouri. 63103, USA). For each pot, above ground biomass was separated from below ground biomass. If there was more than one plant was growing in the pot, then the above ground biomass was also separated by crop type as well. Plant biomass was oven dried for 72 + hours, and then weighed.
DNA extraction
Closely following Qiagen’s protocol, total genomic DNA (gDNA) was extracted from 0.25 g of surrounding bulk soil in a Qiagen QIAcube instrument (Germantown, Maryland, USA) using Qiagen PowerSoil Pro ® DNA kits. Any roots and their respective adhering soil were removed from the bulk soil that was to be used for DNA extraction. Elution volume for extractions was of 100 μl. An Invitrogen Qubit fluorometer (Waltham, Massachusetts, USA) quantified DNA concentrations with high sensitivity assay solutions. Bulk soil samples (n = 103) taken from each of the 21 treatments had 4–5 replicates that were randomly selected for DNA extraction. Controls used were pre-extracted Zymo gDNA (Zymo Research Corporation, California, USA) (n = 2), extracted HPLC water (n = 2), PCR 2 HPLC water (n = 2), and pre-extracted and sequenced soil (n = 2).
Oxford nanopore library prep, sequencing, and bioinformatics pipeline
Extracted DNA was diluted 5 times with HPLC water based on Qubit concentrations (ng/μl). Bacterial primers used were Bact_27F-Mn (5′ –TTTCTGTTGGTGCTGATATTGCAGRGTTYGATYMTGGCTCAG—3′) and Bact_1492R-Mn (5′ACTTGCCTGTCGCTCTATC TTC TACCTTGTTACGACTT—3′). Polymerase chain reaction (PCR) settings were 98 °C for 30 s, 98 °C for 15 s, 50 °C for 15 s, and 72 °C for 1 min for 25 cycles, and 72 °C for 5 min. After the first PCR, equal volumes of DNA and beads were mixed. A 96-pronged magnetic stand was used to move beads with adhering DNA into two 30 s rinses of 70% ethanol. DNA was eluted in a 96-well plate with 40 µL PCR grade water, and beads were removed using a magnetic stand. DNA was quantified using a Qubit with high sensitivity assay solutions. The second PCR settings were 98 °C for 30 s, 98 °C for 15 s, 62 °C for 15 s, and 72 °C for 1 min for 25 cycles, and 72 °C for 5 min. After the second PCR, DNA and barcodes (EXP-PBC-96) were pooled in AMPure bead solution in a 96-well plate. Wells with suspended DNA and barcodes were pooled into a clean Lo-Bind tube. MinION sequencer was loaded with a flow cell (R9.4.1). To prepare the flow cell, air (~ 20 µL) was removed using a pipette. The flow cell was then primed with flush buffer, and pooled DNA was loaded into the sampling port. MinKNOW software was used to sequence the pooled library for 48 h. Raw data was base-called and demultiplexed using Guppy v6.0.1 and reads were then filtered by quality (Filtlong minimum length: 1000; mean quality: 70) and length (Cutadapt: -m 1000 -M 2000). Bacterial taxa were identified using EMU NCBI Reference Database. Sequencing data was processed using DADA2 which removed all singletons by default. EMU error correction removed identified bacterial taxa based on alignment and abundance profiles, such that bacterial taxa with < 1 per 10,000 reads were removed. Sequencing data came from three separate sequence runs, which were pooled for data analysis.
Statistical analysis
Statistical analyses were run, and figures were made using RStudio Version 1.4.1103. Rarefaction curves show that samples plateaued (Fig. 7). Normality for the biomass was tested using the Shapiro–Wilk normality test for normality. Linear models of residuals were used to assess the equality of variance. One-way analysis of variance followed by the Tukey HSD test were used to denote the compact letter display to indicate significance using emmeans, multcompView, and dplyr packages (Lenth, 2021; Wickham et al., 2020; Graves et al., 2019). PERMANOVA was used to find significant differences between treatments and visualized with a constrained Principal Coordinate Analysis (PCoA) with Bray–Curtis dissimilarity index used as a distance from the Vegan package (Oksanen et al., 2007). Betadisper from the Vegan package was used to measure the homogeneity of multivariate dispersions. Differential abundance analysis was based on bacterial species counts using log2 fold change with the Benjamini–Hochberg method (Hagenfeld et al., 2018) using the fdr (false discovery rate) function at an adjusted p-value threshold of 0.05. Alpha diversity was visualized using the Shannon diversity index through the phyloseq package using rarified data (M = 31,960, SD = 11,508, reads per sample) (McMurdie & Holmes, 2013).
2. Identify RD-tolerant rootstocks and the soil microbiome changes associated with RD development/resistance in greenhouse and on-farm conditions
Rootstock Greenhouse Experiment
For the rootstock genotype experiment, the growth of 7 Prunus spp. rootstocks of variable vigor ('Hansen 536', 'Trio-2507', 'Trio-2207', 'Krymsk86', 'MP-29', 'RootPac20', and Controller6) were compared to RD susceptible 'Lovell' trees in disrupted and non-disrupted RD soil as described above for the cover crop experiment (ssee close-up pictures of the rootstock genotypes and the diversity that represent).
Rootstock Orchard Experiment
The most promising rootstocks from the greenhouse experiment that were providing options for use in replant locations with vigorous or semi-dwarfing character for use in low or high densities were to were ordered from Fowler Nursery to be propagated and grafted with 'Cresthaven' as the scion. These rootstocks included 'Trio-2507', 'Trio-2207', 'Krymsk86' which were planted in WCRC-OM (experimental orchard) and in Talbott's Mountain Gold, LLC commercial orchard. After communications with growers collaborators and since 'Lovell' was the most susceptible rootstock in the greenhouse trial we decided to use the 3 options ('Trio-2507', 'Trio-2207', 'Krymsk86') that showed promise in the greenhouse and directly plant them in orchards that previously had peach trees both in the experimental to the commercial site. Planting performed in April 12, 2022 in WCRC-OM and April 13, 2022 in Talbott's Mountain Gold, LLC. Trees were planted in differing densities according to their vigor classifications to form different 2 dimensional canopies with 1 (SSA, super slender axe), 2 (bi-axe) or 4 (quad-axe) leaders that are particularly interesting for our industry (see configuration of training systems here). We modified this part of the experiment according to the industry needs at that time and to preserve resources towards finding solutions for sustainable peach production in a region with limited land suitable for fruit production. The map of the planting in WCRC-OM site can be seen here (Map of 2022 Replant Rootstocks x Training Systems Trial). Data collection included tree survival, trunk cross sectional area (TCSA, cm2) and root suckers. This trial will be maintained after the end of this project for yield and fruit quality data collection and effective communication with stakeholders.
Executive Summary
Replant syndrome (RS) of fruit and nut trees causes reduced tree vigor and crop productivity in orchard systems due to repeated plantings of closely related tree species. Although RS etiology has not been clearly defined, the causal agents are thought to be a complex of soil microorganisms combined with abiotic factors and susceptible tree genetics. Different soil disinfection techniques alleviate RS symptoms by reducing the loads of the deleterious microbiome; however, the positive effect on crop growth is temporary. Here, the current understanding of RS in orchards from a soil microbiome perspective is reviewed. The resolution to RS will require experts to outline explicit descriptions for its symptoms, determine its etiology, identify the primary phytopathogens, and fully explore sustainable treatments which alleviate RS. Two sustainable treatments of RS were selected to explore at a deeper level, soil disinfection and increasing crop diversity to observe what technique could help establish a healthy soil bacteriome. In a greenhouse study, soil disinfection via autoclave was then followed by cover cropping. It was found that soil disinfection increases plant biomass as compared to the control for only the first crop cycle while non-autoclaved soils with a history of cover cropping alleviated RS in RS-susceptible ‘Lovell’ peach seedlings. Although soil disinfection via autoclave was found to distinctly alter the peach soil bacteriome for the full duration of the study, this sustainable practice mimicking solarization failed to provide relief from RS for peach seedlings. Instead of long-term benefits, differential abundance comparisons displayed a loss of potentially beneficial bacteria due to soil disinfection. Paenibacillus castaneae and Bellilinea caldifistulae werebeneficial bacterial species which uniquely colonized peach rhizosphere of non-autoclaved soils with a cover crop history. As a promising sustainable technique, a greater understanding of how inter-/intra-specific competition of cover crops can influence the bulk soil bacteriome was pursued. Alfalfa, brassica, and fescue were grown in 7 different plant combinations (1. alfalfa, 2. brassica, 3. fescue, 4. alfalfa-brassica, 5. alfalfa-fescue, 6. brassica-fescue, 7. alfalfa-brassica-fescue) across 3 density concentrations (low: 1–3 plants, medium: 24 plants, and high: 48 plants) for a greenhouse microcosm experiment. It was found that even in highly competitive space beneficial bacteria were enriched, however, there was an apparent trade-off where different plant combinations enriched distinct beneficial bacteria. As an example, even if a free-living nitrogen fixing bacteria such as an Azospirillum spp. was enriched in the bulk soil of alfalfa and brassica monocultures, it was not enriched in the bulk soil of an alfalfa-brassica plant mixture. Instead Pseudarthrobacter phenanthrenivorans, a phytohormone producer, was enriched in alfalfa-brassica plant mixtures. When zooming into the rhizosphere compartment of these microcosms, it was found that regardless of plant neighbor identity or density, a few rhizobacteria were highly correlated with a specific plant species. Meanwhile, certain plant species specific rhizobacteria were enriched only if specific conditions such as plant neighbor identity or density were met. Overall, our research found that growing genetically distinct plants prior to the re-establishment of a peach orchard could alleviate RS symptoms. Furthermore, that cover crops can enrich for different microbes when grown together as opposed to when grown separately. Lastly, although plants recruit a particular set of bacteria, this recruitment can shift depending on plant neighbor identity or density. Further study of cover crops may identify how they can alleviate RS in orchards worldwide.
Rootstock performance was also evaluated in greenhouse and on-farm conditions 'Hansen 536', 'Trio-2507', 'Trio-2207', 'Krymsk86', 'MP-29', 'Rootpac20', and 'Controller6' were compared to RD susceptible 'Lovell' trees in autoclaved and non-autoclaved RD coming from 10 year-old peach orchard. Among the 8 rootstocks tested all Prunus hybrids appeared to be more tolerant to RD from 'Lovell' and following grower recommendation an experimental orchard a commercial orchard trials were planted in spring 2022. The trial consisted of RD tolerant hybrid rootstocks of varying vigor ('Trio-2507', 'Trio-2207', 'Krymsk86') that could be suitable for efficient production systems of reduced labor requirement. The 2022 Replant Rootstock and Training Systems Trial was established based on the information and concepts of this project and will be a pilot trial that could led to new developments for the future of sustainable peach orchard management.
Objective 1: Evaluate the influence cover and rotation crops on soil health and RD-mitigation on susceptible clonal peach seedlings in greenhouse conditions and on-farm
Effect of Soil Disruption on Cover Crop Growth in Greenhouse
Effect of Soil Disinfection on Cover Crop Biomass
The Kruskal–Wallis test for cover crop above-ground biomass shows the soil treatment (autoclaved vs. non-autoclaved) is significant, χ2 = 16.398 (df = 1, p-value < 0.001). All cover crops grown in autoclaved soils have a higher biomass than cover crops grown in non-autoclaved soils. Biomass of corn (p < 0.001), fescue (p < 0.001), and tomato (p < 0.001) are significantly different between their respective autoclaved and non-autoclaved treatments. However, alfalfa crop biomass is not significantly different (p = 0.459) between the autoclaved and non-autoclaved soil treatments (Figure 1). Alfalfa’s biomass in non-autoclaved soils shows a 34.6% reduction compared to alfalfa grown in autoclaved soils. Corn grown in autoclaved soils has the highest biomass out of all autoclaved and non-autoclaved cover crop treatments. In this study, tomato plants grown in untreated soils (RS soils) have a biomass reduction of 85.8% compared to tomato plants grown in autoclaved soils. This supports the trend that soil disinfection improves plant health.
Effect of Cover Crop and Biomass Incorporation on the Soil Microbiome
The bulk soil bacteriome where cover crops had been growing for 12 weeks were analyzed. The perMANOVA test shows that crop type (p = 0.001), autoclaved and non-autoclaved (p = 0.001), and the interaction (p = 0.03) between the two factors are significant and the CAP axes explain a total of 37.9% of the variance for all samples (Figure 2A). Separation between autoclaved and non-autoclaved soils is clear along axis 1 and explains 29.9% of the variance. For cover crops, autoclaved soils (average distance to median: 0.400) have a lower dispersion than non-autoclaved soils (average distance to median: 0.411) (Figure 2A). Within the autoclaved soil treatment, cover crop bulk soil microbiomes overlap while cover crop treatment has a greater role in shaping the microbiome in non-autoclaved soils. Corn grown in autoclaved soils has the highest biomass after 12 weeks of growth, but its microbiome does not show clear separation from the other cover crops. In the bulk soil of the cover crops, no-cover-crop controls overlap with the crop treatments in either of their respective soil treatments.
Microbes from the autoclaved soil treatment of cover crop bulk soils were of interest due to the increase in biomass in all cover crops. Although the positive effects of autoclaving on cover crop growth are primarily due to the removal of or reduction in potentially negative microorganisms, this study aimed to identify beneficial bacteria instead of highlighting deleterious bacteria that has been previously studied. Thus, differential abundance between non-autoclaved and autoclaved cover crop bulk soils highlights bacteria whose abundances are significantly different (Tables 2 and 3). There are 14 bacterial taxa whose abundance is driven by autoclaving, since all cover crops and no-cover-crop controls share these microbes. No common bacteria are found to be promoted within just the four crop treatments (indicated by the orange circle in Figure 2B). Autoclaved treatments with the highest unique bacterial taxa are no crop (n = 48) and fescue (n = 33), followed by tomato (n = 13), corn (n = 7), and alfalfa (n = 3). In addition, non-autoclaved cover crop bulk soil treatments show 26 bacterial taxa whose abundances are higher than in autoclaved soils and are shared within all cover crop treatments (Figure 10).
Bulk soils after the cover crop had been incorporated and decomposed for two weeks continued to show separation between autoclaved and non-autoclaved microbiomes (Figure 3). The perMANOVA test shows that cover crop history (p = 0.001) and autoclaved soil treatment (p = 0.001) are significant and explain a total of 35.7% of the variance. The interaction between cover crop history and autoclaved soil treatment is not significant (p = 0.105) (Figure 3). Similar as in crop history, the microbiome corresponding to bulk soil after cover crop incorporation shows a tighter cluster in autoclaved soils (Figure 3). Interestingly, the incorporation of alfalfa biomass in non-autoclaved soils shows an independent cluster compared to other cover crop treatments. Bacterial drivers (identified by the differential abundance) of the incorporated cover crop bulk soil microbiome that are found in all crops and no-cover-crop controls continue to have increased abundance in their respective autoclaved soil treatment (Figure 3). In autoclaved cover-crop-incorporated soils, the bacterial species Tumebacillus soli, Cytobacillus oceanisediminis, and Mesobacillus subterraneus are found to be bacterial drivers of the microbiome (Figure 3) and these are the same microbes found in autoclaved cover crop soils (Table 2). In non-autoclaved soil incorporated with cover crops, the bacterial species Vicinamibacter silvestris, Skermanella stibiiresistens, Bacillus megaterium, Nostoc sp. HK-01, and Nostoc sp. PCC 7107 are primarily found (Figure 3), which are also present in non-autoclaved cover crop soils (Table 3).
Effect of Soil Disinfection and Cover Crop Incorporation on Peach Growth
The Kruskal–Wallis test for peach dry total biomass shows that the soil treatment effect (autoclaved vs. non-autoclaved) is significant (χ2 = 35.298, df = 1, p-value < 0.001). Biomass is higher for peach trees grown in soil that has not been disinfected via steam autoclave, and it is observed for most soil cover crop treatments, with alfalfa being the exception (p = 0.095) (Figure 4A). Pairwise comparisons using corn (p = 0.002), fescue (p < 0.001), and tomato (p < 0.001) cover crops show a significant difference in biomass within autoclaved and non-autoclaved soil treatment pairs (Figure 4A). Between the two no crop controls that later had peaches growing, there was no significant difference in biomass within autoclaved and non-autoclaved soil treatments (p = 0.363) (Figure 4B). Additionally, autoclaved soils with a cover crop history of fescue (p < 0.001) and corn (p = 0.002) perform worse than autoclaved soils with a history of no cover crops (Figure 4B). Within autoclaved soil treatments, peaches grown in alfalfa and tomato have a higher biomass than peach trees in soils that previously had corn and fescue. In all, peach trees grown in non-autoclaved soils have the highest biomass compared to peaches grown in autoclaved soils.
Nitrogen and Nutrient Analysis
Nutrient analyses were performed to investigate if nutrient cycling could help explain the differences between autoclaved and non-autoclaved soil treatments. Dry peach biomass was used in regression plots with several different soil nutrient parameters. Soil nutrient parameters that are not significant predictors of peach biomass are total organic carbon, organic nitrogen, organic C/N ratio, total phosphorus (H3A), available phosphorus, potassium (H3A), and available potassium. The only positive correlation between dry peach biomass is with available organic nitrogen (R2 = 0.144, p-value = 0.038) (Figure 5). Available nitrogen is statistically different by crop treatment with alfalfa and tomato having higher available nitrogen than fescue and no cover crop treatments (Figure 11). Overall, alfalfa and tomato treatments have the highest available nitrogen and are not statistically different compared to corn treatments. Fescue and no crop treatments have lower available nitrogen (Figure 5). The only negative correlation between dry peach biomass found to be significant is with ammonium (Figure 12).
Effect of Soil Disinfection, Cover Crop Incorporation, and Peach Growth on the Bulk and Rhizosphere Soil Bacteriome
Shannon index of controls and all treatments separated by soil and cover crop history shows a trend of non-autoclaved soils having a greater alpha diversity than autoclaved soils (Figure 8). For beta diversity, the autoclave soil treatment is the driver of cluster separation. Non-autoclaved and autoclaved soil bacteriomes remain separated for the entire study (cover crop bulk soil, cover crop incorporation bulk soil, peach bulk soil, and peach rhizosphere).
The bacteriome corresponding to bulk soil of peach grown under autoclaved (average distance to median: 0.402) and non-autoclaved (average distance to median: 0.387) conditions show that dispersion is greater in disinfested soils. The perMANOVA test shows that cover crop history (p = 0.001) and autoclaved soil treatment (p = 0.001) is significant and the CAP axes explain a total of 32.5% of the variance. The interaction between cover crop history and autoclaved soil treatment is not significant (p = 0.369) (Figure 6A). This result indicates how the autoclaved bacteriome continues to be prone to change, and the separation along axis 2 supports clustering by cover crop history (Figure 6A). Under both soil treatments, it is observed that previous cover crop histories create bacterial associations. Peaches grown in non-autoclaved soils with a cover history of alfalfa create a unique bacteriome and have the highest shift from the non-autoclaved centroid than other non-disinfested treatments (average distance to median for: alfalfa, 0.356; fescue, 0.343; tomato, 0.340; no cover crop, 0.327; corn, 0.279).
Peach seedlings with the highest biomass correspond to non-autoclaved soil treatments. Therefore, microbes from non-autoclaved peach bulk soils are of the most interest. Within all non-autoclaved treatments (cover crop history and the no-cover-crop history control) for the peach crop cycle, there are seven bacterial species (Bacillus megaterium, Brevitalea aridisoli, Brevitalea deliciosa, Gaiella occulta, Nitrospira japonica, Skermanella rosea, Skermanella stibiiresistens) whose abundance increases compared to autoclaved peach bulk soils (Table 4). Soils with cover crop histories have no additional bacterial species in common that do not increase in the no cover crop treatment (indicated by the orange circle; (Figure 6B). Non-autoclaved treatments with the highest unique bacterial species that increase compared to autoclaved soil correspond to no crop and fescue.
In contrast to the peach bulk soil, the rhizosphere soil corresponding to non-autoclaved treatment shows a tighter cluster than the rhizosphere soil of autoclaved soils (Figure 7A). Peaches grown in autoclaved soils (average distance to median: 0.319) loosely cluster based on previous cover crop and show overlap. Within non-autoclaved soils (average distance to median: 0.36), peaches that previously had a cover crop of alfalfa or fescue samples have the greatest shift away from the no crop control. The constrained PCoA shows that cover crop history (p = 0.001) and autoclaved soil treatment (p = 0.001) are significant and explain a total of 50.6% of the variance. The interaction between crop history and autoclaved soil treatment is not significant (p = 0.267) (Figure 7A). Similar to the peach bulk soil, the soil history of alfalfa grown in non-autoclaved soils develops a distinct bacteriome.
From the differential abundance of non-autoclaved peach bulk soil, out of the seven microbes found in all treatments, six of these bacterial species (Bacillus megaterium, Brevitalea aridisoli, Brevitalea delicios, Gaiella occulta, Nitrospira japonica, Skermanella rosea) are found again in all non-autoclaved peach rhizosphere soil treatments, with Skermanella stibiiresistens being the exception (Tables 4 and 5). Differential abundance of non-autoclaved peach rhizosphere soil per crop shows that there are 11 shared bacterial species that increase in abundance by soil treatment, regardless of cover crop (Figure 7B). Soils with cover crop histories have two additional bacteria species, Paenibacillus castaneae and Bellilinea caldifistulae, in common (Figure 7B). Bacterial species that are in higher abundancies in non-autoclaved peach rhizosphere soils with a cover crop treatment and not found in the RS symptomatic non-autoclaved soils without a crop control are Bacillus cereus (alfalfa, fescue, and corn), Paenibacillus xylanilyticus (fescue, corn, and tomato), Baekduia soli (corn and tomato), Terrimonas suqianensis (corn and tomato), Desulfobulbus propionicus (fescue and corn), Paenisporosarcina indica (alfalfa and corn), Desulfopila inferna (alfalfa and fescue), and Desulfotalea psychrophile (alfalfa and fescue) (Table 1). Non-autoclaved no-crop treatments, notably, have the highest unique bacterial taxa (n = 53). Of the cover crops, corn (n = 10) and fescue (n = 10) have the highest counts of unique bacterial taxa in higher abundances, with tomato (n = 8) and alfalfa (n = 5) having the least (Figure 7B).
Soil microbiome composition under increasing cover crop densities
Monoculture
In monoculture, total biomass of all three cover crops increased with crop density. Total biomass of alfalfa increased significantly within each density increment and had the highest biomass of any cover crop for densities of 24 plants and 48 plants (Fig. 8). Brassica above ground biomass did not statistically differ between 1 and 24 plants but required a density of 48 plants to raise the total biomass. Fescue biomass at a density of 24 plants was double the same at a density of 1 plant; the biomass was not significantly different between 24 and 48 plants. For all three cover crop types, a single plant density yielded the largest individual plant, and the number of plants-to-biomass ratio was inversely proportional to density.
Surrounding bulk soil bacteriome analysis
Bacterial microbiome shifts in the surrounding bulk soil of the plants were assessed using alpha and beta diversities, and differential abundance of specific taxa. For Shannon Diversity Index, there was no consistent visual trend of an increasing alpha diversity measure by density or diversity (Fig. 9). PERMANOVA model with all data combined showed both density and diversity were significant factors along with their interaction (Fig. 10). Low to high plant densities of alfalfa induced significant (p = 0.001, R2 = 0.286) shifts on the surrounding bulk soil bacteriome (Fig. 1b, Table 6). As an estimate of beta diversity, the average distance to the median for the bacterial bacteriomes at a density of a single alfalfa plant was 0.2893, for 24 alfalfa plants 0.3251, and for 48 alfalfa plants 0.2841. The density of 48 alfalfa plants had the highest clustering out of the three densities. Increasing densities of brassica induced a significant (p = 0.01, R2 = 0.183) shift on the surrounding bulk soil bacteriome (Fig. 1d, Table 6). For brassica, average distance to the median for the bacteriomes had the highest clustering for the single (0.2861), 24 (0.3165), and 48 (0.3047) plant densities. The PERMANOVA test showed that when looking at fescue by increasing densities of 1, 24, and 48 plants, the shift induced on the soil bacteriome was significant (p = 0.002, R2 = 0.208) (Fig. 1f, Table 6). For fescue, the average distance to the median for the bacteriome had the highest clustering for the single plant density with 0.2862, with 24 fescue plants at 0.3227, and 48 fescue plants at 0.3367. The increasing density of the alfalfa monocrop explained the highest variability (CAP1 + CAP2: 28.6) as compared to brassica (18.3) and fescue (20.8) (Fig. 1b,d,f).
Differential abundance comparisons of the bacteriome in the surrounding bulk soil were conducted when there was a significant difference in total plant biomass per pot as density increased (Table 1). Bacteria of interest were those which were highlighted by the differential abundance comparison in conjunction with an increase in total plant biomass. Alfalfa with a low density (one plant) was enriched for 22 bacterial taxa compared to medium (24 plants) and high density (48 plants) microcosms. Alfalfa with a medium density was enriched for 9 bacterial taxa compared with high density microcosms. Alfalfa with a high density was enriched for 13 bacterial taxa compared to both medium and low-density microcosms. Brassica with a low density (one plant) was enriched for 7 bacterial taxa compared to high density (48 plants) microcosms. Brassica with a medium density (24 plants) was enriched for 3 bacterial taxa compared to a high-density microcosm. Fescue with a low density (one plant) was enriched for 8 bacterial taxa compared to medium (24 plants) and high density (48 plants) microcosms. Fescue with a medium density was enriched for 4 bacterial taxa compared to low density microcosms. Fescue with a high density was enriched for 5 bacterial taxa compared to low density microcosms.
Mixtures of two plants
Alfalfa plant biomass increased when grown in polyculture with higher densities of brassica (Fig. 2a) or fescue (Fig. 3a). Fescue’s biomass did not significantly increase in densities of 24 and 48 plants with either alfalfa or brassica in paired mixtures (Figs. 3b and 4b). However, fescue had the highest biomass in a cover crop mixture with just alfalfa (Fig. 3b). The trend of fescue biomass in a cover crop mixture with alfalfa was similar to fescue growing in monoculture (Figs. 1e, 3b, 4b), where there was a significant increase followed by a leveling off in crop biomass. Brassica’s biomass in cover crop mixtures did not change with increasing densities (Figs. 2b, 4a). Overall, the average total above ground dry biomass was highest in the alfalfa and fescue cover crop mixture at a density of 48 plants.
For Shannon Diversity Index, increasing plant-plant intra/inter specific competition did not increase microbial alpha diversity in the surrounding bulk soil (Fig. 9). The PERMANOVA test showed that alfalfa and brassica mixtures under increasing densities of 2, 24, and 48 plants, induced a significant shift on the soil bacteriome (p = 0.01, R2 = 0.332) (Fig. 2d, Table 6). For alfalfa and brassica mixtures, the average distance to the median for the bacterial microbiomes at a densities of 2 (0.305), 24 (0.272), and 48 (0.2586) plant mixtures had the highest clustering for the 48-plant density (Fig. 2d). Bacteriomes of alfalfa and brassica mixtures showed decreasing dispersion as density increased. The PERMANOVA test showed that when looking at alfalfa and fescue mixtures, increasing densities of 2, 24, and 48 plants induced a significant shift on the soil bacteriome (p = 0.02, R2 = 0.192, Table 6) (Fig. 3d). For alfalfa and fescue mixtures, the average distance to median for the bacterial microbiomes at a density of two plant mixtures (0.3073), 24 plants (0.3067), and 48 plants (0.3240) had the highest clustering for the 24-plant density. The PERMANOVA test showed that when looking at brassica and fescue mixture by increasing densities of 2, 24, and 48 plants, the shift induced on the soil bacteriome was significant (p = 0.038, R2 = 0.203) (Fig. 4d, Table 6). For brassica and fescue mixtures, the average distance to median for the bacterial microbiomes at a density of two plant mixtures (0.2849), 24 plants (0.2571), and 48 plants (0.2755) had the highest clustering for the 24-plant density. The increasing density of the alfalfa and brassica crop mixtures explained the highest variability (CAP1 + CAP2: 33.2) as compared to alfalfa and fescue (19.1), and brassica and fescue (20.3) (Figs. 2d, 3d, 4d).
Differential abundance analysis of the bacterial microbiome in the bulk soil was performed only if there was a change in the total biomass of a crop within the mixture (Table 2). Alfalfa and brassica mixture in low density (2 plants) showed an enrichment of 26 bacterial taxa compared and high density (48 plants) microcosms whereas high compared to low densities showed an enrichment of 2 bacterial taxa. Alfalfa and fescue mixture in low density (2 plants) showed an enrichment of 8 bacterial taxa compared to high (48 plants) and medium density (24 plants) microcosms whereas high and medium densities showed an enrichment of 13 bacterial taxa as compared to low density microcosms. There was no biomass increase for the total biomass for brassica and fescue mixture, and the biomass change for fescue was used instead to highlight bacteria with significant differential abundances. Brassica and fescue mixture in low density (two plants) showed an enrichment of 8 bacterial taxa compared to high density (48 plants) microcosms whereas high densities showed an enrichment of 1 bacterial taxon as compared to low density microcosms. Brassica and fescue mixture in medium density (24 plants) showed an enrichment of 5 bacterial taxa compared to high density (48 plants) microcosms whereas high densities showed an enrichment of 6 bacterial taxa as compared to medium density microcosms.
Mixtures of three plants
The mixture with three different cover crops showed similar trends as when they were grown in the cover crop mixtures of just two crops. When all three plants were grown together, there was a higher biomass for alfalfa and fescue as density increased, while there was no increase in biomass for brassica (Fig. 5b). For alfalfa, this trend was different that the previous cover crop mixtures and in monoculture since the density increase of 24 to 48 plants did not show an increase in biomass. Fescue biomass remained as the lowest in the mixture of three crops (Fig. 5c). The number of brassica plants did not influence the total amount of above ground biomass for brassica. In summary, the biomass trends of mixtures of three cover crops followed previous trends for the mixtures of two cover crops.
Surrounding bulk soil bacteriome analysis
Biomass of an individual plant was largest in the density of three plants and decreased as density increased (Fig. 3c) Mixtures with all crops of a low density (3 plants) to medium density (24 plants) both showed an enrichment of 1 taxon (Table 3). Mixtures with all crops of a low density (3 plants) to high density (48 plants) showed an enrichment of 11 taxa, while high densities had an increase of 2 taxa as compared to low densities (Table 3). The PERMANOVA test showed that when looking at fescue by increasing densities of 3, 24, and 48 plants, the shift induced on the soil bacteriome was not significant (p = 0.069, R2 = 0.183) (Fig. 5e, Table 6). For the mixture of all three plants, the average distance to median for the bacterial microbiomes at a density of three plants (0.3123), 24 plants (0.3035), and 48 plants (0.3279) had the highest clustering for 24 plants.
2. Identify RD-tolerant rootstocks and the soil microbiome changes associated with RD development/resistance in greenhouse and on-farm conditions
Rootstock performance was also evaluated in greenhouse and on-farm conditions 'Hansen 536', 'Trio-2507', 'Trio-2207', 'Krymsk86', 'MP-29', 'Rootpac20', and 'Controller6' were compared to RD susceptible 'Lovell' trees in autoclaved and non-autoclaved RD coming from 10 year-old peach orchard. Among the 8 rootstocks tested all Prunus hybrids appeared to be more tolerant to RD from 'Lovell' (see Figure below).
Figure. Prunus spp. rootstock above and bellow ground fresh biomass by soil treatment. The mean of all the different rootstocks was compiled to visualize how effective soil disruption was to increase rootstock biomass. Rootstocks grown in disrupted (sterile) soils developed slight higher biomass than rootstocks grown in un-disrupted (non-sterile) soil. The only rootstock that developed significantly higher biomass as a result of the soil disruption was the RD susceptible 'Lovell'. These data were also presented in the 2022 X International Peach Conference in a Poster Presentation (see here in details)
Following grower recommendation an experimental orchard a commercial orchard trials were planted in spring 2022. The trial consisted of RD tolerant hybrid rootstocks of varying vigor ('Trio-2507', 'Trio-2207', 'Krymsk86') that could be suitable for efficient production systems of reduced labor requirement. The 2022 Replant Rootstock x Training Systems Trial was established in and experimental orchard in WCRC-OM (see here) and in a commercial orchard (Talbott's Mountain Gold, see here) based on the information and concepts of this project and will be a pilot trial that could led to new developments for the future of sustainable peach orchard management (see pictures of the establishment of the 2022 Peach Replant Rootstock x Training Systems Trial).
Following 2 growing seasons we see that 'Krymsk86' is the most vigorous rootstock under replant conditions in both sites with 17.5 cm2 TCSA and 17.1 cm2 TCSA in WCRC-OM and the commercial orchard, respectively. Both 'Trio-2207' and 'Trio-2507' and had 10.1 cm2 TCSA in WCRC-OM and 13.8 and 13.1 10.1 cm2 TCSA in the commercial orchard. Mortality was pretty low the first 2 years after planting in WCRC-OM across all rootstocks with 'Trio-2507' and 'Trio-2207' exhibiting 3% tree loss and 'Krymsk86' 5%. In the commercial orchard mortality of 'Trio-2207' was significantly higher (29%) compared to 5% for 'Trio-2507'. 'Krymsk86' lost zero trees in the commercial orchard. These results indicate superiority for 'Krymsk86' as a replant tolerant rootstock and show promise that the semi-dwarf 'Trio-2507' could be an option for intermountain peach production systems. More years of study are required for solid conclusions and grower recommendations for the semi-dwarfing options. The effect of the training system was also captured in these initial years of establishment, however, it its too early to make final conclusions as the first peach fruit set his expected in 2024 in these trees.
Replant disease (RD) is characterized by reduced crop productivity resulting from repeated plantings of genetically related crops. This globally relevant disease is thought to be primarily caused by soil borne pathogenic microorganisms with specialized antagonistic traits towards the specific crop. We hypothesize that using cover crops or different rootstock genotypes grown in disrupted soils could be employed to beneficially alter the microbiome of RD soils for peach orchards. Steam autoclaving was used to disrupt the soils to amplify the microbiome interactions in the soil. Four different crops (corn, tomato, fescue, and alfalfa) were grown in disrupted and non-disrupted RD soil from Grand Junction, CO under greenhouse conditions. We show that soil disruption significantly increased biomass of all crops (alfalfa p= 0.0415; corn p < .0001, fescue p= 0.0019, and tomato p < .0001). Cover crops were reincorporated into the soil and subsequently RD susceptible 'Lovell' peach saplings were planted. After 12 weeks, trees in non-disrupted soils were significantly larger (height p= 0.0055, diameter p < .0001). Crop type alone had no significant impact on tree size, however when considering soil sterilization, alfalfa in non-sterilized soil resulted in increased tree height, and total leaves. These preliminary results suggest that alfalfa could alleviate peach trees in RD soil and is currently established in the experimental orchards for field evaluations. For the rootstock genotype experiment, the growth of 7 Prunus spp. rootstocks of variable vigor ('Hansen 536', 'Trio-2507', 'Trio-2207', 'Krymsk86', 'MP-29', 'RootPac20', and 'Controller6') were compared to RD susceptible 'Lovell' trees in disrupted and non-disrupted RD soil. Peach trees in disrupted soils grew larger (height p = 0.0001, diameter p = 0.0022). Controller6, Krymsk86, MP-29, Trio-2507, 'Trio-2207', Hansen 536 and RootPac20 may be RD resistant by showing above and below biomass growth compared to RD susceptible 'Lovell' but no significant differences between soil treatment. Combining RD resistance performance and vigor classification 'Krymsk86', 'RootPac20' and 'Trio-2207' were selected for field evaluations. Future studies will reveal if a shift of the microbiome can be correlated with peach health, to develop a cropping technique that can be applied to other tree fruit systems with RD.
Research Outcomes
A robust population of beneficial bacteria are needed to remedy RS soils. Non-autoclaved soil cultivated with alfalfa, corn, and tomato as cover crops developed the best conditions for peaches to withstand RS in this study. This further supports the idea that certain cover crops may be deployed to reduce RS, specifically for peaches. Paenibacillus castaneae and Bellilinea caldifistulae, which were cultivated exclusively in the rhizosphere of non-autoclaved soils by peaches for only cover crop histories, may be beneficial and further study could shed light on their role as general colonizers that can possibly reduce RS. Non-autoclaved bulk soils and peach rhizospheres also have an increased abundance of Bacillus megaterium, Gaiella occulta, and Nitrospira japonica. However, these bacterial taxa are also present in the non-autoclaved and no-cover-crop control, which did not outperform the non-autoclaved cover crop treatments (alfalfa, corn, and tomato) in terms of biomass. Nonetheless, further research should be conducted to determine the role of these bacteria in alleviating RS, as these bacteria could be specifically recruited by peaches since abundances are present in the peach rhizosphere in all non-autoclaved treatments. Future studies should use mock community inoculations to investigate the robustness of these bacterial species, since bacteriomes function as a consortia and may require one another to reduce RS.
In contrast, soil disinfection instigates the loss of bacterial species with populations unable to recover within the time frame of this study. This gives an insight into the possible consequences of effective soil disinfection techniques. The Shannon index shows how the newly autoclaved RS bulk soil control has a drastically reduced alpha diversity compared the initial untreated RS bulk soil control. However, the Shannon index supports the fact that many bacterial populations are able to recover by the time the cover crops have grown . This is in line with previous studies that saw benefits of reducing microbial load using soil disinfection techniques and immediately planted peach trees. Here, it is proposed that moderate soil disinfection should be used to avoid removing beneficial microbes by using temperatures that are high enough to be lethal to poor soil competitors such as phytopathogens, but low enough for beneficial bacteria to recolonize. The present study shows that cover crops can help ameliorate RS symptoms, but not all cover crops provide equal benefit, with soil disinfection benefits being temporary.
In addition, modern Prunus hybrid genotypes of variable vigor classification provide sustainable options for peach fruit growers in the intermountain west region. Rootstocks like Krymsk86 seem to be tolerant to replant conditions and can be used in the calcareous soils of intermountain west without the need for prior soil fumigation.
Education and Outreach
Participation Summary:
In order to develop critical materials for supporting our educational and outreach methods regarding sustainable replant disease management a review paper with all the concepts was published. The information published in this paper allowed the team to develop concept educational materials regarding the etiology of replant disease through a biological point of view. The concepts of this paper structured the main grower dissemination materials (see here an example).
Over the duration of the project and given the severe impacts of COVID-19 pandemic for the first couple of years (2020-21) on outreach activities globally, our team managed to offer several educational and outreach activities. These activities included: 2 workshops, 1 webinar, 1 field day, was well as more than 10 oral presentations in grower meetings and seminars. Except of the local outreach with growers several presentations (oral and poster) were given in different scientific meetings at departmental local, national and international level.
The focus of the workshops and outreach was in overall peach orchard management with responses to current crises (e.g., 2020 spring and fall freeze damage) and provide relevant and sustainable consultation to local growers.
Products of this work included:
1 PhD Dissertation that was defended in December 13, 2023.
3 refereed scientific journal articles with open access to the general public were published (see links below).
1 referred scientific journal article submitted and currently under review (review here).
1 CSU Extension fact sheet submitted and currently under review.
PhD Dissertation
Newberger, D. R., 2024. BACTERIOMES OF PEACH ORCHARD SOIL AND COVER CROPS. PhD Dissertation, Colorado State University, Department of Horticulture and Landscape Architecture. Defended December 13, 2023.
Journal articles
Pieper, R. J., Anthony, B. M., Chaparro, J. M., Prenni, J. E., Minas, I. S. 2023. Rootstock vigor dictates the canopy light environment that regulates 1 metabolite profile and internal fruit quality development in peach. Under Review in Plant Physiology and Biochemistry.
Paper on the impact of training system and rootstock genotype and vigor on peach fruit productivity was published in September 2021. Anthony BM, Minas IS, 2021. Optimizing Peach Tree Canopy Architecture for Efficient Light Use, Increased Productivity and Improved Fruit Quality. Agronomy 11 (10), 1961.
Paper on the impact of semi-dwarf and replant tolerant rootstocks on peach production and quality was presented in the ISHS XII Orchard Systems Symposium and submitted for publication in July 2021. Minas, I.S., Reighard, G.L., Brent Black, B., Cline, J.A., Chavez, D.J., Coneva, E., Lang, G., Parker, M., Robinson, T., Schupp, J., Francescato, P., Jaume Lordan, J., Tom Beckman, T., Shane, W., Sterle, D., Pieper, J., Cathy Bakker, C., Clark, B., Ouellette, D., Swain, A., Winzeler, H.E. 2021. Establishment performance of the 2017 NC-140 semi-dwarf peach rootstock trial across 10 sites in North America. Acta Horticulturae 1346, 669-676.
Extension Fact Sheets
Extension Fact Sheet on Peach Rootstocks submitted to CSU Extension in August 2023 and is currently under review from external reviewers. Pieper, J., Anthony, B.M., Minas, I.S. Suitable peach rootstocks for western Colorado growing conditions. CSU Extension under review.
Talks at grower meetings
Minas IS, Orchard & Environmental Factors Affecting Peach Productivity & Harvest Quality, Great Lakes EXPO, Grand Rapids, MI, December 2021.
Minas IS, Orchard & Environmental Factors Affecting Peach Productivity & Harvest Quality, Western Colorado Horticultural Society Annual Meeting, Grand Junction, CO, January 2022.
Minas IS, 2017 NC-140 Semi-Dwarf Peach Rootstock Trial, International Fruit Tree Association Annual Meeting, Hersey, PA, February 2022.
Pieper JR, The influence of rootstock genotype on peach productivity and fruit quality. Western Colorado Horticultural Society Annual Meeting, Grand Junction, CO, January 2022.
Anthony, BM, Influence of Training Systems on Peach Vigor, Production and Fruit Quality. Western Colorado Horticultural Society Annual Meeting, Grand Junction, CO, January 2022.
Talks at scientific meetings
Minas IS, Reighard GL, Black B, Cline JA, Chavez DJ, Coneva E, Lang G, Parker M, Robinson T, Schupp J, Francescato P, Lordan J, Beckman T, Shane W, Sterle D, Pieper J, Bakker C, Clark B, Ouellette D, Swain A, Winzeler HE. Establishment performance of the 2017 NC-140 semi-dwarf peach rootstock trial across 10 sites in North America. Oral Presentation at ISHS XII International Symposium on Integrating Canopy, Rootstock & Environmental Physiology in Orchard Systems, Wenatchee, WA, July 2021.
Newberger D, Vivanco J, Minas IS. Using soil disruption followed by cover crops and rootstocks to alleviate peach replant disease. Poster Presentation at ISHS X International Peach Symposium, Naousa, Greece, May 2022.
Webinars
Minas, IS and Sterle DG, Webinar due to COVID-19 restrictions on CSU Pomology Research Program at Western Colorado Research Center at Orchard Mesa (WCRC-OM) updates in February 2021 under the frame of Western Colorado Horticultural Society (WCHS) with 65 attendees.
Workshops
Workshop and field demonstration to educate growers and disseminate research findings on Principles of Fruit Production for Beginners Growers was offered at Western Colorado Research Center at Orchard Mesa (WCRC-OM) in January 17, 2022 under the frame of Western Colorado Horticultural Society (WCHS) with 45 attendees.
Field Demonstrations
Field demonstrations of Rootstocks, Pruning and Training Systems in Peach was offered at Western Colorado Research Center at Orchard Mesa (WCRC-OM) in February 21, 2022 with 45 attendees.
Short Course for Pruning & Crop Load Management: Peach Training Systems & Frost Damage Mitigation with demos was offered at Western Colorado Research Center at Orchard Mesa (WCRC-OM) in February 21, 2022 with 50 attendees.
Field days
Annual Pomology Field Day 2022 to cover peach rootstocks, training systems, cover crops and replant disease management in May 9th, 2022 with 45 attendees.
Education and Outreach Outcomes
- soil health
- orchard replant disease
- cover crops
- beneficial microbes
- rootstocks