Recipient Organization
CORNELL UNIVERSITY
(N/A)
ITHACA,NY 14853
Performing Department
Microbiology
Non Technical Summary
Composting is an effective means for waste reduction, nutrient recycling, and soil conditioning. Recent research suggests that co-composting traditional organic waste streams with additives such as biochar, chitin, and lipids might improve the nutrient content and disease suppressive qualities of compost. I will work with three farms to assess the impact of additives like biochar, crustacean shells, and spent mushroom wastes (depending on availability locally) on their compostsperformance with respect to plant growth.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
Composting is an effective means for waste reduction, nutrient recycling, and soil conditioning. Recent research suggests that co-composting traditional organic waste streams with additives such as biochar, chitin, and lipids might improve the nutrient content and disease suppressive qualities of compost. I will work with three farms to assess the impact of additives on their compost performance.Composts aren't all the same! I did my sabbatical in 2017 at Scott's Miracle-Gro Inc evaluating the potential of microbial additives to improve the performance of compost-derived plant growth media. For many years, peat served as the main starting material for commercial plant growth media, but peat reserves are dwindling and its mining is not sustainable. Thus, many producers are turning to compost as an alternative. As one of the country's largest suppliers of plant growth media, Scott's knew of the inherent variability in the compost they sourced that differed by climatic conditions, inputs, and management. They hoped that adding a superbug might reduce that variability. Even poorly performing compost (from a plant perspective), however, is teaming with billions of microorganisms better adapted to their compost environment than any microbial additive could be. Research in my own lab and those of others supports 3 general observations 1) compost microbial community composition (the who) is variable, 2) microbial community function (the how) is fairly conserved, and 3) microbial additives rarely survive inoculation. Emerging research suggests, however, that compost function, especially plant relevant functions like nutrient supply and pathogen suppression, can be pushed in beneficial directions by incorporating specific additives. Two that have received attention recently in scientific literature are biochar and chitin, but just because they work in the lab or greenhouse, isn't a guarantee that they will work on the farm. They need to be tested in real-world conditions.A recent meta-analysis of biochar ammendments to compost showed biochar, crop, and soil specific outcomes (https://doi.org/10.1016/j.scitotenv.2019.06.244), thus more work is clearly needed to understand the potential impact of this and other additives on local farms. My three farm collaborators are eager to conduct field scale experiments at their respective operations. I plan to work with each of them to help them produce compost on-site at their facilities that is engineered to provide improved nutrient delivery and better pathogen suppression than the compost they are currently producing. Specific additives will depend on the target crop, the specific history of pathogens encountered on that farm with that crop, and the availability of locally sourced materials. It will be modeled after a recently completed study in Europe (https://www.mdpi.com/2073-4395/9/5/225), but will include an additional element of cross validation in which each farm's compost will also be tested at the other farms and in the green house at Cornell's Ithaca campus in order to determine if local success or failures can be generalized to other locations. Examples of additives to be co-composted include, but are not limited to biochar, insect/crustacean waste, and spent mushroom bedding. Compost performance will be measured with on-site growth trials of high value vegetable crops chosen by the farmers. Endpoints will be compared to on farm-controls using each farmer's standard composting formulation/ and will include compost maturity (https://doi.org/10.1680/jees.2013.0063), crop yield & quality, disease incidence, soil pH, soil fertility (C, N, P, K), soil biomass, and soil respiration. Disease incidence will be assessed in consultation with Dr. Gary Bergstorm, plant pathologist at Cornell. Selected compost samples taken during the production processes and of compost-amended soils will also be subjected to metagenomic analysis of DNA extracted directly from the compost. Unsequenced samples will be archived so that they can be sequence when additional funds become available from other funding sources anticipated from follow-on applications such as USDA NIFA Foundation that will build on this work. Metagenomic analysis will be conducted as done in our recent description of compost from a local commercial composting facility (https://doi.org/10.1007/s00253-018-9201-4).
Project Methods
Composts will be analyzed the Cornell Nutrient Analysis lab for the following:pH, Organic Matter, Modified Morgan Extractable P, K, micronutrientsSoluble saltsActive CarbonWet Aggregate StabilityRespirationTotal Carbon, Total Nitrogen (C/N ratio)PredictedAutoclave-Citrate Extractable (ACE) Protein TestPredictedAvailable Water CapacitySurface, sub-surface hardness interpretation (Optional: You provide the penetrometer readings.)Cress germinationThe methods for microbial community analyses are similar to those I've recently published: (https://doi.org/10.1016/j.envpol.2019.07.018)Genomic DNA will be extracted from compostusing the E.Z.N.A.® stool DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer's instruction.The V3-V4 regions of the bacterial 16S rRNA gene were amplified with the universal primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GACTACHVGGGTWTCTAAT-3′). The primers for each sample will included a unique 8-nucleotide "barcode" sequence to allow for post-sequencing sample identification. The PCR program will be as follows 95°C for 5min, 25 cycles at 95°C for 30s, 56°C for 30s, and 72°C for 40s with a final extension of 72°C for 10min followed by a hold at 4°C. PCR reactions were performed in triplicate 25?μL mixtures containing 2.5?μL of 10?×?Pyrobest Buffer, 2μL of 2.5mM dNTPs, 1μL of each primer (10?μM), 0.4 U of Pyrobest DNA Polymerase (TaKaRa), and 15?ng of template DNA. Amplicons will be extracted from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, U.S.) according to the manufacturer's instructions and quantified using QuantiFluor™ -ST (Promega, U.S.). Purified amplicons will bepooled in equimolar ratios and paired-end sequenced (2?×?300) on an Illumina MiSeq platform according to standard protocols by Beijing Allwegene Tech, Ltd (Beijing, China) using the Illumina Miseq PE300 sequencing platform (Illumina, Inc., CA, USA).DNA sequence and community analyses will beperformed with the QIIME. Briefly, raw sequences will be filtered based on sequence length and quality score. Low-quality sequences will be removed while those receiving an average quality score <20 over a 50 bp sliding window were truncated. Any native or truncated reads shorter than 150 bp will also also removed along with those containing 2 nucleotide mismatches in primer and those that had ambiguous characters. Only sequences in which the forward and reverse overlapped by more than 10 bp will beassembled. Reads which could not be assembled will bediscarded.The unique sequence set will beclassified into operational taxonomic units (OTUs) using a threshold of 97% identity via UCLUST. Chimeric sequences will be identified and removed using Usearch (version 8.0.1623). The taxonomy of each 16S rRNA gene sequence fromUCLUST will be compared against the Silva119 16S rRNA database using the confidence threshold of 75%Statistical analysis: All chemical and biochemical analyses will be made from the three independent replicates and the significant difference between mean values was calculated by ANOVA followed by the Tukey honest significant difference (HSD) test using SPSS 21.0. Microbial community analyses were performed using QIIME (in-house python script), Microsoft Excel, R (v 3.3.3) and based on all reads (without rarefaction). Shannon index values of samples will becalculated at the OTU level by alpha_rarefaction.py in QIIME. The relative abundance of OTUs will becalculated on actual sequence counts (not rarefied) for each sample, then summed at related taxonomic levels from genus to phylum prior to beta-diversity analyses. Morpheus will be used to draw the heatmap with clustering based on Spearman rank correlations of shared OTUs (https://software.broadinstitute.org/morpheus). Principal Component Analysis (PCA) will be based on observed OTUs and plotted using ClustVis. Unit variance scaling will beapplied to samples; singular value decomposition with imputation will beused to calculate principal components . Prediction ellipses computed by ggplot2 will represent that probability (0.95) that a new observation from the same group would fall inside the inscribed area. Univariate analyses for associations between different composts will be conducted using LEfse and visualized in a cladogram. PICRUST will be used to infer functional pathways based on bacterial composition . STAMP will be used to group and visualize significantly impact pathways identified via PICRUST.Plant growth will be measured on the basis of dry weight and fruit yeild (both mass and number). To account for plant variability, 10 reps will be performed in the green house. We will aim for 5 reps in the field, but the actual number will be dependent on the farmers space and labor limitations. Statistical correlations between plant growth, compost properties, andmicrobial community characteristics, will be performed as described above.