Progress report for GNE21-255
This project is part of a broader research program titled "Transitioning to organic grain production: strategies to increase viability and ecosystem services while reducing risks and obstacles," which is sponsored by the National Institute of Food and Agriculture (NIFA). The ultimate aim of the research project is to help boost the competitiveness of transitional organic grain crop producers by identifying system parameters that positively impact soil quality, crop yields, practicality and profitability. Microbiological indicators of soil health in the four transition systems will be the focus of this NE-SARE project. The following are the specific objectives:
1. Examine the effects of four organic transitional strategies on the composition and diversity of microbial communities.
The four systems will be contrasted in terms of the types of prokaryotic communities (bacteria) and fungi, as well as their relative abundance and diversity. We hypothesize that the microbial community structure and diversity will differ among the systems and minimal tillage systems with higher biomass diverse cover crops will favor more microbial diversity.
2. Study the effect of the transition strategies on potential microbial functionality.
We’ll study the relative abundances of enzyme encoding genes involved in certain functions and link that to potential microbial functionality (e.g. C degradation, N cycling) in the four systems by using databases such as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database and ITS gene data.
3. Investigate the relationship between microbes and soil physical-chemical properties.
We will correlate the microbial diversity and individual microbial taxa with the soil physico-chemical properties.
In the larger study, the results from this study, as well as other bioindicators like earthworm, slug, and arthropod populations and carbon and nitrogen lability will be assessed. A soil health index will be developed using all of the bioindicators, in addition to chemical and physical characteristics of the soil.
The purpose of this project is to add a biological component of soil health to the evaluation of four organic transition strategies. Specifically, I want to document the transition strategy (soil disturbance, soil cover, and input cost) that will best contribute to the diversity and functionality of the microbial community in the soil system. To achieve these objectives, multi-farm experiments are being conducted as a part of a larger NIFA grant project at four different sites in Maryland. Because the larger project (of which the NESARE project will be a sub-study and enhancement) is designed to assess soil health impacts of the four transition strategies with a focus on soil physical and chemical properties, the NE-SARE grant will explicitly be used to deepen the biological dimension of soil health investigation by assessing the microbial parameters using molecular techniques.
Maryland grain farmers are showing increasing interest in organic production due to potential economic (much higher grain prices) and environmental (no synthetic pesticides) benefits. The three-year transitional period required for organic certification is a major barrier for prospective organic producers because they are limited in the methods allowable to control weeds, manage pests and diseases and supply plant nutrients (Delate and Cambardella, 2004). Particularly in Maryland, most conventional grain farms practice some form of no-till agriculture, grow cover crops and use strict nutrient management plans. This controlled nutrient, minimum tillage, cover cropping “eco-friendly” starting point in Maryland begs the question of whether transitioning to the organic farming practices from the “conventional” practices would cause an increase or decrease in environmental impacts and soil health. The answer most likely depends upon the system of practices (inputs, soil disturbance, soil cover, etc.). In Maryland, it is therefore essential to identify the organic transition strategies that can match or exceed the soil health and environmental benefits of the no-till with cover crops grain production system it would be replacing.
The strategies being compared and the microbial parameters proposed in this study will generate information to help farmers identify what management practices best contribute to the bio-indicators of soil health. Data on microbial diversity could infer nutrient cycling rates and defense against pathogens in a particular system (Fierer et al., 2020). Identifying microbial bio-indicators of several soil processes like nitrification, cellulose degradation, etc can be cheaper and easier alternatives to other direct methods (Fierer et al., 2020). Microbes respond rapidly to management practices, which can act as early indicators of changes in soil properties pertaining to the health of the environment (for example: abundance of a nitrifier taxa can indicate greater risks of losses of soil N via nitrification, leaching and potential groundwater pollution). Thus, the analysis of community structure, diversity and functionality of microbial taxa in the four systems will give a clearer picture of their relative productivity and sustainability and will largely help in “fine-tuning” agronomic methods to the benefit of soil biology, overall environmental health and quality of agricultural systems.
The experiment includes three or four treatments (depending on the location) that follow a continuum of soil disturbance and cover crops. The treatments are: 1) Perennial alfalfa-grass hay, untilled; (UT) 2) Minimum-till corn-soybean-wheat rotation with precision-zoned high biomass diverse cover crops (MT); 3) Reduced-till corn-soybean-wheat with high-biomass cover crops (RT); and 4) Traditional full-tillage organic soybean-corn, with simple cover crops and input substitution (FT).
In January 2020, field-scale plots were developed in four different locations in Maryland, including both university experiment station land and commercial farms. Three locations are on two different soil types that reflect the coarser and finer textured soils found in Delmarva's grain cropping coastal plain area; the fourth has loamy soils in the hilly Piedmont region. In the commercial farms, three transition systems (MT, RT, and FT) are being compared, and four systems are being studied in experiment stations (NT, MT, RT and FT). The systems are being tested in randomized complete block experiments, with the treatments repeated four times on each farm. The individual treatment plots range in size from 30 ft x 300 ft to 60 x 150 ft, large enough to allow commercial scale farm equipment to be used. In each treatment, two representative and relatively uniform sampling areas for collecting soil and plant samples have been demarcated.
In early April 2020 the transition to organic farming began at all four sites by planting a uniformly-managed spring oats crop that was harvested in July, 2020. In July-August 2020, composite soil samples were collected in each half of each treatment plot using a 2.8 cm diameter x 30 cm long cores which were subdivided into 0-10, 10-20 and 20-30 cm depth increments. This set of soil samples represent the baseline soil condition before establishment of the four transition treatments. These baseline soil samples are being tested for the physical and chemical properties. Subsamples of the baseline soil samples were saved unground and frozen at -20 C for the possibility of soil microbial DNA extraction. During the NE-SARE study period, soil samples will be collected after the crop harvest in the second and third year of transition in spring of 2022 and 2023. For each treatment, ten cores will be taken from a single experimental replicate and composited. Cores will be taken at least 2 m from the edge of the plot and will be distributed throughout the whole plot. Cores will be taken using a 2.8 cm soil probe. Debris will be removed from the soil surface and the probe will be inserted to a depth of 10 cm. The core will be removed intact when possible and placed in a Ziploc bag. The core or loose soil will be wrapped tightly in the bag, sealed and immediately placed on ice. Gloves, probe, and knife will be wiped clean and sterilized with 70% isopropyl alcohol spray between treatments. Samples will be transported to the laboratory in a cooler and frozen immediately upon arrival. The baseline soil samples collected in fall 2020 and the samples from spring of 2022 and 2023 will be used in microbial analysis.
Total microbial DNA from each soil sample will be extracted using 250 mg of fresh soil mass using the DNeasy PowerLyzer PowerSoil Kit (QIAGEN Sciences Inc, Hilden, Germany) following the manufacturer’s recommendations. Subsequently, DNA extracted from each sample will be quantified using a Qubit 2.0 fluorometer (Invitrogen) and stored at -20 °C. When needed, DNA concentration for each extraction will be diluted to 5ng/μl for PCR amplification.
For prokaryotic communities, a fragment of the 16S ribosomal ribonucleic acid (rRNA) gene will be targeted using the primers 515F/806R and fungal internal transcribed spacer (ITS) gene regions will be targeted using the primer ITS1f/ITS2r (Coporaso et al., 2011; White et al., 1990). Illumina sequencing will be performed according to the 16S metagenomic sequencing library preparation manual (Part number 1504423 rev. B, Illumina, Inc.). PCR cleanup and indexing will be carried out using AMPure CP beads (Beckman Coulter, Pasadena, CA) and Nextera XT 96 index kit (Illumina, Inc.), respectively. Samples will be pooled; the amplicon library size will be verified using a Bioanalyzer 2100 DNA chip (Agilent Technologies). The library will be quantified using qPCR and the final library will be run on an Illumina MiSeq using a 600-cycle v3 cartridge.
The R package dada2 (version 1.14.1; Callahan et al., 2016) will be used for processing fastq files returned from Illumina Sequencing (filter and trim, dereplication, sample inference, merging pair-end reads, and removing chimeras). Prior to dada2 workflow, sequences containing ‘N’ values (unreadable bases) will be pre-filtered and the forward and reverse primers will be removed using cutadapt (version 2.9; Martin, 2011). Taxonomy will be assigned to the chimeric-free sequence table with the dada2 native implementation of the naïve Bayesian classifier method. The SILVA (version 132; Quast et al., 2013) and UNITE (release 10.10.2017; Nilsson et al., 2019) databases will be used as reference sequences for the identification of prokaryotic 16S rRNA genes and fungal ITS gene amplicon sequence variants (ASV), respectively. The ASV tables will be converted into phyloseq objects using the R phyloseq package (McMurdie and Holmes, 2013).
Before performing microbial community analysis, features with ambiguous phylum annotation (phylum classified as ‘NA’) and phyla with low-prevalence will be removed. In addition, ASVs available in less than 10% of the total samples will be discarded; low-performing samples with total sequence reads less than 1000 will also be discarded. To account for differences in sequencing depth across samples, samples will be rarefied by randomly sub-sampling the minimum number of sequence reads present in the sample.
For Objective (1), the Shannon diversity index will be calculated as a measure of α-diversity using the R phyloseq package. The Bray-Curtis dissimilarity (β-diversity) will be calculated after transforming the count data into relative abundances. Non-metric multidimensional scaling (NMDS) ordination plots based on Bray-Curtis dissimilarity will be constructed to visualize differences in prokaryotic and fungal community structure between the treatments. The effect of treatments on microbial community structure will be statistically tested using the permutational multivariate ANOVA (PERMANOVA) with 1000 permutations via the adonis function of the R vegan package (Oksanen et al., 2019). To check the assumption of PERMANOVA, permutation-based tests of multivariate homogeneity of group dispersions will be performed using the betadisper and permutest functions of the vegan package. If the overall treatment effect is found significant based on PERMANOVA, pairwise treatment comparisons will be performed using the pairwiseAdonis package in R (Martinez Arbizu, 2019).
The associations between the most abundant taxa and the microbial community structure will be investigated using the envfit function of the R vegan package, and the taxa that shows significant correlations will be fitted to the NMDS plots. The VennDiagram package in R will be used to construct venn diagrams to depict the number of unique and shared ASVs between soil samples for each treatment (Chen, 2018).
For objective Objective (2), the potential metabolic functionality of the prokaryotic community will be predicted based on 16S rRNA gene data using the t4f (Tax4Fun) function of the R themetagenomics package and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (Woloszynek et al., 2017). The relative gene abundances will be calculated by summing all KEGG orthologs associated to a specific metabolic pathway (C cycling, N cycling, etc) (Lüneberg et al., 2018). Similarly, FUNGuild will be used for functional guild and trophic mode assignments to the ITS gene data for the fungal community (Nguyen et al., 2016). For simplicity, we will classify the assigned fungal trophic modes into four categories: Pathotrophs (plant pathogens, fungal parasites), Saprotrophs, Pathotroph-Saprotrophs, and others (animal pathogens, symbiotrophs). As recommended by FUNGuild developers, only trophic modes assigned with ‘highly probable’ or ‘probable’ confidence ranking will be reported. Kruskal-Wallis and Dunn’s test will be used to statistically test the effect of treatments on microbial diversity and potential microbial functionality of the soil microbiota using the R rstatix package (Kassambara. 2020). Significant differences will be determined at α value of 0.05, unless otherwise stated. Objective (3) The physico-chemical parameters of the soil (such as pH, organic carbon, active carbon, bulk density, structural stability) measured from the larger study will be correlated with the bacterial diversity (OTUs number – number of identified operational taxonomic units). Also, a Pearson’s correlation analysis will be done to determine the interaction between the soil physico-chemical properties and identified microbial taxonomic groups (e.g. Order, Phyla), using R software.
The data on microbial diversity and metabolic functionality for each of the treatments will be compared across sites and over the time points to assess the consistency in response of microbes. Correlation of biological parameters with the physico-chemical properties will be used to understand the possible contrast in microbial response. A multivariate CLUSTER will be used to distinguish differences in the overall community structure involving tens to hundreds of microbial taxa. Because of the complex variables involved, a quality index will be developed to simplify the tracking and comparison of changes in soil health among the four treatments. Several biological quality functions will be chosen (such as the microbial diversity, nitrifiers, denitrifiers, plant-growth promoting rhizobacteria, etc) and corresponding indicator properties will be assigned functional scores. The t-scores for each variable will be weighted and then averaged to give a relative quality index.
Education & Outreach Activities and Participation Summary
We are collaborating on the NIFA project with commercial growers, extension agents, and a non-profit farmer organization called Future Harvest which promotes sustainable agriculture. As the NESARE grant will cover an important component of the larger research on soil health metrics, we will integrate and share the results, observations, and progress reports of this grant in all outreach activities pertaining to the larger project. Specific to this project, we intend to reach the farmers and extension agents at various stages of project implementation through extension bulletins and blog posts, which will include details on what, why and how of our project, and our observations. An extension article titled "Interested in $10 corn and $30 soybeans for certified organic, but not sure how to transition?" was published in the November 2021 edition of University of Maryland Extension newsletter "Agronomy news". The article consisted of information on general introduction to the project and some preliminary results from the larger project. In future, I will give talks on “Bioindicators of soil health in organic transition systems” at regional conferences and workshops, including the annual Future Harvest meeting in January 2023, where many regional farmers and food system professionals participate. We will organize a field day to bring farmers, extension agents, and the scientific community to one of our experiment sites in the summer of 2023 to evaluate the four systems in action and explain our research efforts and findings. In the field day, we’ll examine crops in the field, evaluate soil health properties associated with microbial action (structure, porosity, presence of visible fauna) as well as use pictures, pamphlets, fact sheets, and give talks to elaborate our experimental approach and results. An assessment of the response of soil microbial communities to different organic transition strategies, along with recommendations as to the best strategy, will be the topic of at least one extension fact sheet or bulletin. As results become available, information will be included in extension newsletters (i.e. UMD extension’s Agronomy news). Project results will also be reported at the meetings of professional societies (e.g., the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America tri-societies international annual meetings during 2022 and 2023). After the conclusion of the study, results will be published in a peer-reviewed journal (e.g., Agronomy Journal or Crop Science).