Progress report for GW21-228
Project Information
Nitrogen is a critical nutrient for fertilizing our crops that can be acquired non-synthetically via symbiotic nitrogen fixing bacteria associating with legumes. Legumes are widely used in crop rotations, and there is growing interest in using them in intercropping systems. Effective nitrogen fixation and nitrogen cycling depends on the microbial community, so it is important to understand how our agricultural practices impact these microbial communities. This project will determine how a mixed cropping system of pea and canola impacts microbial communities in the soil, rhizosphere, and roots in this system. Analysis of the diversity of the microbial communities will be done to determine the abundance of nitrogen fixing bacteria, how well they are performing, and how they are contributing to the growth and nitrogen content of pea and canola crops. Results from this study will be available to farmers and stakeholders via talks and posters at producer-focused workshops, conferences, field days, and more. The expected outcomes from this project are (1) to have a clearer understanding on how the way we grow our crops is impacting microbial communities, (2) to have suggestions for the farming community in Eastern Washington to improve the performance and abundance of nitrogen fixing bacteria in order for them to produce their crops more sustainably and efficiently, and (3) to help guide further research on nitrogen fixing bacteria as a means for nitrogen fertilization and to obtain a more sustainable and productive method of farming, while improving and maintaining soil health.
Our research objectives are:
- To determine if the peaola intercropping system that we are studying is producing similar results for yield and land equivalent ratio (LER) as other studies have found.
- To determine the biodiversity and abundance of the nitrogen fixing bacteria of the microbial populations present in soil, rhizosphere, and root samples collected from pea and canola in monoculture and intercropped, and with varying levels of nitrogen fertilizer application.
- To determine if there is a difference in the community structure and importance of nitrogen fixing bacteria in the microbial communities present in the different cropping systems, including the symbiotic ability of the pea-associated nitrogen-fixing bacteria.
- To determine the nitrogen content of the crops grown in monoculture and in the peaola intercropping system to determine whether symbiotic nitrogen fixation by pea can meet the nitrogen needs of intercropped canola.
Our primary education/outreach objective is:
To improve sustainability and productivity of the agricultural system across the inland Pacific Northwest by quantifying crop yield, changes to the soil and plant microbiome, and economic benefit when peaola is adopted as a rotational crop in a dryland wheat-based system.
Our education/outreach sub objectives are:
- To guide agricultural practices in the inland Pacific Northwest through sharing the results of this study with producers and agricultural stakeholders.
- To increase the awareness of the general public in the inland Pacific Northwest about the benefits of intercropping and peaola.
- To guide future research on intercropping and peaola in the inland Pacific Northwest.
For us to accomplish the various experiments and events we describe in this proposal, we will follow the below timeline. It should be noted that some of the steps for this project will happen and have happened before the start of this grant’s funding.
WSARE Grant Proposal Application: February 9th
First sample collection at Mark Greene’s farm: April-May 2021
First crop yield analysis: April-May 2021
First sample nitrogen tests: April-May 2021
First DNA extractions: May-June 2021
Initial sequencing and qPCR: June-July 2021
Data analysis of first samples: July-December 2021 (Could need an even longer of period to perform the data analysis due to the presented difficulty of data normalization)
Preparation of outreach materials based on first set of results: December 2021-April 2022
Second planting and growth of strip plots at Mark Greene's farm: September 2021-April 2022
Potential submission of an initial manuscript: December 2021-April 2022
Second sample collection at Mark Greene’s farm: April-May 2022
Second crop yield analysis: April-May 2022
Second nitrogen tests: April-May 2022
Second DNA extractions: May-June 2022
Second qPCR: June 2022-July 2022
Data analysis of the second samples: July-December 2022 (Same conditions as first round)
Tri-societies meeting: November 6th-9th, 2022
Regional soil health meeting: Winter 2022
Analysis of combined data: December 2022-January 2023 (May happen earlier depending on ease of data analysis)
Prepare final outreach materials: January 2023
Work on submitting more comprehensive manuscript: February 2023-August 2023
Attended and contribute to outreach events and platforms discussed in our education plan: February 2023-August 2023
Cooperators
- (Researcher)
- - Producer
- (Researcher)
- (Researcher)
Research
The objectives of our project are:
- To determine if the peaola intercropping system that we are studying is producing similar results for yield and land equivalent ratio (LER) as other studies have found.
- To determine the biodiversity and abundance of the nitrogen fixing bacteria of the microbial populations present in soil, rhizosphere, and root samples collected from pea and canola in monoculture and intercropped, and with varying levels of nitrogen fertilizer application.
- To determine if there is a difference in the community structure and importance of nitrogen fixing bacteria in the microbial communities present in the different cropping systems, including the symbiotic ability of the pea-associated nitrogen-fixing bacteria.
- To determine the nitrogen content of the crops grown in monoculture and in the peaola intercropping system to determine whether symbiotic nitrogen fixation by pea can meet the nitrogen needs of intercropped canola.
Land Equivalent Ratio and Yield Analysis
For our first objective, peaola plot trials were planted across three different years (2020, 2021, and 2022). Peaola planted in 2020 was located at a farm near Colfax, WA where the plots were 11 m by 61 m. These plots were planted and sampled prior to the project start date, but are being included due to their relevance to the overall project and final datasets. In 2021 and 2022 peaola was planted at a WSU research farm near Davenport, WA where the plots were 2 m by 8 m and at Mark Greene's farm where the plots were 12 m by 152 m. At the WSU research farm, the 2021 and 2022 plots were seeded into no-till winter wheat chemical fallow using a Fabro double disk no-till drill. The winter pea variety Goldenwood (ProGene Plant Research, LLC) and the canola variety Plurax (Rubisco Seeds) were used in both the monoculture and intercrop as both varieties have been successfully grown in the Palouse. In the intercrop, peas and canola were planted in the same row at the same time. Planting occurred in late August as a compromise between the pea and canola ideal planting periods. The whole plot yield was sampled, using a M-2B clipper mill from A. T. Ferrell & Company Bluffton, Indiana which could separate the peas and canola. The plots near Colfax, WA were planted on April 9th, 2020, into stubble from the previous year's winter wheat crop using a no-till Cross Slot drill. Again, the winter pea variety used was Goldenwood (ProGene Plant Research, LLC) and a Clearfield spring-canola variety (DynaGro 200 CL) was used. Strips were harvested on September 14th, 2020 and weighed using a weigh wagon. A 1 kg subsample was collected from the grain stream which then had peas and canola separated using an M-2B clipper mill from A.T. Ferrell & Company Bluffton, Indiana. Peas and canola were weighed separately and used to calculate the relative pea and canola yield. At the Green Farm in 2021 and 2022, 12 m by 152 m large strip trials were seeded into chemical fallow winter wheat stubble on April 20th, 2021, and May 17th, 2022. Banner variety spring pea purchased from ProGene Plant Research, LLC and canola NCC101s (Photosyntech) were used. A horsch hoe style winged opener was used to seed the plots, with the peas being sent down the dry fertilizer shoot in the mid-row and canola being placed an inch deep by the winged openers.
Land equivalence ratio (LER) was calculated using Equation 1, where ICp and ICc represent the yields of intercropped pea and canola, respectively, and Mp and Mc represent the yields of monoculture pea and canola, respectively. Mp and Mc were calculated through the mean of all four replicates for peas and canola, respectively. The LER for each individual peaola plot was calculated using the same Mp and Mc for the corresponding year.
Equation 1: LER = (ICp/Mp) + (ICc/MCc)
Microbial Community Analysis
To accomplish our second objective, we have collected samples from small strip plots (2 m x 8 m) at a WSU research farm near Davenport, WA and large strip trials on Mark Greene’s farm (12 m x 152 m) near Cloverland, WA and at farm located near Colfax, WA (11 m x 61 m). The large strip trials at the Greene farm consisted of 1 peaola plot in 2021 and 2 peaola plots in 2022 to accommodate for the needs of the producer. Large strip trials located near Colfax, WA consisted of 4 replicates of peaola (51 kg N ha-1), 4 replicates of pea (0 kg N ha-1), and 4 replicates of canola (101 kg N ha-1). The small plot trials near Davenport, WA in 2021 were comprised of peaola at three N rates (4 replicates at 0 kg N ha-1, 4 replicates at 51 kg N ha-1, and 8 replicates at 101 kg N ha-1), 4 replicates of pea (0 kg N ha-1), and 4 replicates of canola (101 kg N ha-1). In 2022 the small plot trials near Davenport, WA consisted of 36 replicates of peaola, 4 replicates of pea, and 4 replicates of canola. N application rates were originally given as percentages in the proposal due to uncertainty in the needed application rate at the time the proposal was written. Therefore, the values 0%, 50%, and 100% N refer to 0, 51, and 101 kg N ha-1 respectively as the percentages represented steps in the application rate. The number of replicates varies from what was indicated in the proposal due to changes in the needs of the producer, Mark Greene, and the agronomist, Isaac Madsen. These changes were needed by Mark Greene due to limited availability of space for growing crops. Isaac Madsen needed to change the experimental design at the Davenport, WA location due to the results of prior experiments which showed a need to investigate the impacts of seeding rates in 2022. For each plot, we collected soil and root samples at 3 different points. Soil was stored at -20C. Rhizosphere was collected from all root samples into rhizosphere buffer and both samples were stored at -80C.
DNA extractions were performed on the collected soil and rhizosphere samples using a Kingfisher DNA extraction machine following the Earth Microbiome Project’s protocol for the QIAGEN® MagAttract® PowerSoil® DNA KF Kit. 8 roots were freeze dried and crushed to a powder before performing DNA extracting using CTAB (a cationic detergent that can extract and purify DNA from bacteria) in order to pilot this method of sample handling. Extracted DNA was sent to Michigan State University’s Research Technology Support Facility for Illumina Amplicon Sequencing of the 16S V4 region on the MiSeq v2 Standard platform, resulting in 250 bp paired end reads. We will be performing qPCR for the nifH gene, which is a functional gene associated with nitrogen fixation and is often used as a marker for nitrogen fixing bacteria. This will be performed using the SmartChip Real-Time PCR System. Through doing qPCR of the nifH gene, we will be able to determine approximately how many bacteria in the population can perform nitrogen fixation.
Sequences were analyzed using QIIME2 version 2021.8 on WSU’s Kamiak High Performance Computing Cluster. Alpha-rarefaction plots showed that our sampling depth was sufficient for analysis of the microbial community. Bacteria were classified using “qiime feature-classifier classify-sklearn” with the Silva 138 99% OTUs from 515F/806R classifier found on the QIIME2 data resources page. After removing sequences representing chloroplasts and mitochondria, the bacterial core microbiome was found using “qiime feature-table core-features”. For nifH, we will be looking at the difference in the abundance of the nifH gene between the samples. This will allow us to determine if there are more nitrogen fixing bacteria present in a particular treatment. Analysis of this data will be done using R version 4.2.1 in RStudio version 2022.07.0+548.
Network Analysis
We performed network analysis on the microbial count data used in objective 2 from Illumina amplicon sequencing of 16S rDNA for our third objective using SPIEC-EASI version 1.1.0 in R version 4.1.0 on WSU’s Kamiak High Performance Computing Cluster. SPIEC-EASI was chosen due to it being acknowledged as an appropriate method to produce a co-occurrence network from microbial abundance data. We used Cytoscape version 3.9.1 to analyze the networks, where bacterial RSVs are represented by individual nodes. Networks were produced for peaola, pea, and canola individually to allow for comparisons of the network structure across the three cropping systems. We will also be generating networks using the above methods, but having each node represent the microbial community at the Phylum level.
Stable Isotope Analysis
To accomplish our fourth objective, we were able to determine how the microbial communities impact the levels of nitrogen present in the plant samples through performing stable isotope analysis, which relates to our fourth objective. To do this, plant shoots were collected at each sampling point across the different trials except for in 2021 due to a communication error. Shoot samples were dried, leaf tissue was collected, and crushed to a fine powder. Stable isotope analysis was then performed at the WSU Stable Isotope Core providing us with C and N percentages for each sample.
Results
Land Equivalent Ratio and Yield Analysis
For our first objective, we found that the cropping system had a significant impact on the yield of pea and canola, and on the LER. Across the trials grown at the WSU research farm in 2021 and the trials grown near Colfax, WA in 2020, it was found that the average LER was 1.63 for peaola compared to the normalized value of 1 for the monocultures. The application of N fertilizers had no significant impact on the yield of peas and canola, or the LER for both of these locations. For the strip trials grown in Colfax, WA, the LER for peaola, at 1.37, was not significantly different from the monocultures. It was found that the yield of canola was not significantly different from when it was grown in monoculture, but the yield of pea was significantly reduced compared to when it was grown in monoculture at the Colfax, WA location. At the WSU research farm there was a significant increase in the LER of peaola from monoculture. However, there was a significant reduction in pea and canola yields from monoculture. At the Greene farm in 2021, no yield data was collected due to poor performance of the peas, so it was considered a loss. Crops planted in 2022 at the WSU research farm and at the Greene farm still have not been harvested, so yield data is not available at this time.
Microbial Community Analysis
To accomplish our second, third, and fourth objectives, we have collected 264 soil samples, 459 root and rhizosphere samples, and 333 plant shoots across 3 different locations from 2020 to 2022. Substantial delays were experienced in regard to accomplishing our second and third objectives due to there being initial issues with our DNA extraction and quality control pipeline. As a result, we are slightly behind schedule in regard to our sequencing timeline, but we are quickly getting back on schedule. To date, we have extracted DNA from all of the soil and rhizosphere samples we collected in 2020 and 2021. DNA extraction is in progress for the samples collected in 2022. Additionally, since we are working on piloting our method for our root samples, we have only extracted DNA from 8 root samples that were collect in 2020. Sequencing has been done on all of the samples collected in 2020, and samples collected in 2021 are currently being sequenced at the Michigan State University Genomics Core along with the 8 root samples. Due to delays in our optimization of our lab's protocol of the SmartChip Real-Time PCR system, we have not performed qPCR for the nifH gene on any of our samples. Once we have optimized this protocol, results should be produced quickly. Network analysis has been successfully done on the sequences for our 2020 samples. Stable isotope analysis for C and N percentages has been performed on the leaf tissue of the shoot samples collected in 2020 and 2022. Analysis of these results has been completed for the 2020 samples, and analysis is ongoing for the 2022 samples. All results can be found bellow.
Data analysis has been done on the 2020 soil and rhizosphere sequences. Through the inclusion of a microbial community standard we determined we were able to detect bacteria at the appropriate abundance down to a relative abundance of 0.089%. When analyzing the soil using Shannon diversity index, Observed Features, and Evenness (measures of α-diversity) we did not find any significant differences (P < 0.05) or trends (0.05 ≤ P ≤ 0.1) between pea monoculture, canola monoculture, and peaola. We did find a trend towards the monoculture pea soil being richer than the monoculture canola soil using Faith's Phylogenetic Diversity index (Kruskal–Wallis Test, n1 = 12, n2 = 12, H = 5.60, p = 0.0537) (Figure 1). No other significant differences or trends were found using Faith's Phylogenetic Diversity index. For the rhizosphere, we found no significant differences (P < 0.05) or trends (0.05 ≤ P ≤ 0.1) between pea monoculture, canola monoculture, and peaola using Shannon diversity index, Observed Features, and Faith's Phylogenetic Diversity index (Figure 2).

We did find that the rhizosphere of the canola monoculture was significantly more even than pea monoculture as determined by Evenness (Kruskal–Wallis Test, n1 = 21, n2 = 22, H = 7.01, P = 0.0485). No other significant differences or trends were observed under Evenness. When looking at the β-diversity of the soil using Jaccard distance, Bray-Curtis distance, and unweighted UniFrac distance, we did not find any significant differences (P < 0.05) or trends (0.05 ≤ P ≤ 0.1) between pea monoculture, canola monoculture, and peaola. Under weighted UniFrac distance, we found that between pea and canola monoculture soils, and between peaola and canola monoculture soils, there

was a trend toward there being a difference in the community composition (PERMANOVA, F = 2.06, P = 0.0705; PERMANOVA, F = 1.72, P = 0.0705) (Figure 3). This was not observed between pea monoculture and peaola soils. When looking at the rhizosphere, we found a significant difference in the β-diversity between canola and pea rhizosphere regardless of cropping system (PERMANOVA, P<0.05), and trends between like intercropped and monoculture crops (PERMANOVA, 0.05≤P≤0.1) based on Bray-Curtis Distance and Jaccard distance (Figure 4). Under weighted UniFrac distance, we found that the trends between like intercropped and monoculture crops were not observed. Using unweighted UniFrac distance, we did not observe the trend between monoculture and intercropped canola, and we saw that only a trend existed between intercropped pea and intercropped peaola (PERMANOVA, F = 1.229370, P = 0.084).

Analyses of the bacterial core microbiomes of our soil and rhizosphere samples were performed. For soil, we analyzed the strict bacterial core microbiome, consisting of bacteria found in 100% of our samples. We found that the peaola core microbiome consisted of 34 members, the pea core microbiome consisted of 23 members, and the canola core microbiome consisted of 29 members. When comparing those microbiomes, we found that 13 bacteria were shared across them. From the canola core microbiome, 8 bacteria were shared with peaola. Out of the pea core microbiome, 3 bacteria were shared with peaola. Despite the peaola microbiome having shared members with monoculture pea and canola, it does have 10 core members that were unique to its own microbiome (Figure 5). When analyzing the rhizosphere, we did not analyze the strict core microbiome because it was not found across all 3 cropping systems. Instead, we analyzed core microbiomes consisting of bacteria found across 90% of our samples since a core microbiome was found across all 3 cropping systems at this level. At this level, we found that the peaola core microbiome consisted of 23 members, the pea core microbiome consisted of 14 members, and the canola core microbiome consisted of 18 members. When comparing those microbiomes, we found that 8 bacteria were shared across them. From the canola core microbiome, 4 were shared with peaola, and from the pea core microbiome, 1 was shared with peaola. Again, we found that despite peaola having shared core members with the microbiomes of pea and canola monoculture, we did find 10 unique members of the peaola core microbiome (Figure 5).
Network Analysis

Network analysis using SPIEC-EASI has been performed successfully at the genus level. It should be noted, however, that identification to the genus level is rare when performing analyses on Illumina amplicon sequences due to the small size of the sequences. The peaola co-occurence network consists of 1016 nodes and 10,058 edges (Figure 6). The pea monoculture network consisted of 984 nodes and 8674 edges and the canola monoculture network consists of 965 nodes and 8330 edges (Figures 7 and 8).

This shows that there is a change in the structure of the microbial community from monoculture pea and canola to peaola, which can also be seen visually (Figures 6, 7, and 8). Although there are nodes that are more highly connected than others, there does not appear to be any one node that is more highly connected than the others. Instead, there are clusters of highly connected nodes found throughout all 3 networks.

Stable Isotope Analysis

After performing statistical analyses on the carbon and nitrogen percentages for the 2020 samples, we determined that there were no significant differences (P < 0.05) or trends (0.05 ≤ P ≤ 0.1) between the intercropped and monoculture pea and canola, and between pea and canola regardless of cropping system (Figure 9).
Discussion
Land Equivalent Ratio and Yield Analysis
It is not surprising that the peaola system did not appear to benefit from increasing the rates of synthetic N fertilizer, as previous work done on legume-oilseed intercropping has found similar results (Porter et al., 2020). Despite these results, more work needs to be done to determine if the peaola cropping system can be grown independently of N fertilizer application. Largely, this is due to there being a potential that N was not a limiting factor in the crops grown in this study. Therefore, future research will be addressing if N is being transferred from peas to canola via plant-plant-microbe interactions. This research will help us determine if these interactions are responsible for the increased LER with decreased synthetic N input of peaola. The relative yield of peas to canola, calculated based on monoculture pea and canola, showed that winter peaola did not strongly favor either pea or canola. Although some studies have found that pea is favored over canola in overyielding peaola, our data align with the majority of studies on peaola with an overall LER of 1.63 with neither pea nor canola being strongly favored (Fletcher et al., 2016). The crop grown in 2021 at the Greene farm likely experienced failure of its pea crop due to extreme weather conditions around the time of flowering. This is because heat is known to decrease yield in both pea and canola, especially around flowering (Mohapatra et al., 2020).
Microbial Community Analysis
Through our analyses of the α-diversity and β-diversity of the microbial communities in the soil and rhizosphere, we did not find that intercropping pea and canola had a large impact on the diversity of the microbial community. Despite this finding, we did find that peaola did have a unique core microbiome compared to pea and canola when grown in monoculture. Taken with our findings for α-diversity and β-diversity, it does show why we did not see a significant difference here as in the core microbiome there does appear to be a combining of the pea and canola microbiomes despite peaola's unique members. Therefore, it would make sense that the diversity would not be significantly different from pea and canola if there were not many unique members added to the peaola microbiome overall. Despite this, our findings that peaola does have unique core members does suggest to us that it is possible that peaola is creating a unique soil environment from pea and canola, potentially due to changes it is making to the soil chemistry. Further work will need to be done in order to confirm this. In addition, since changes were observed in the core microbiome, it is possible that some members of the microbial community are impacted by peaola in terms of their overall abundance. Therefore, we will be performing differential abundance analysis on our sequencing data in order to determine if there are any bacteria that are changing significantly in their overall abundance between cropping systems. This will be done in R version 4.2.1 in RStudio version 2022.07.0+548. Additionally, data on the abundance of the nifH gene across cropping systems from qPCR will also be insightful into how the microbial community is changing across cropping systems, as it will show us if there is a change in the abundance of bacteria with the ability to fix nitrogen.
Network Analysis
Based on the changes in the number of nodes and edges found in the networks from pea and canola to peaola, it is clear that there is a change in the structure of the microbial community. There is also the potential that this is demonstrating the combining of the pea and canola microbial communities in peaola as we saw through our microbial community analysis. Although there is useful information that can be extracted from these networks, we have decided to remake our networks with bacteria at the phylum level instead. This will allow patterns to become clearer in the networks, as this reduces the number of nodes. Further investigation will then be done into phyla that are highly connected in the network to determine the microbial members present in each phylum and their predicted ecological role.
Stable Isotope Analysis

From our analyses of our stable isotope data showing C and N percentages, it is difficult to make any conclusions on how pea and canola intercropping is impacting nutrient transfer between plants. However, it is still unknown if our results will be the same for our analysis on our 2022 leaf samples. With that said, even though these results show no significant difference in the N content of the leaf tissue across cropping systems and between plants, this data does not give us any information on where the N those plants have incorporated came from. Preliminary results from a greenhouse experiment we performed measuring the dry shoot mass of pea and canola across cropping systems suggests that pea is providing N to canola while experiencing no negative consequences to its own success.

This is because we saw that there was no significant difference in the dry shoot mass of pea across cropping systems both with and without a N treatment (Figure 10). When looking at canola, we did see that the dry shoot mass of canola when grown with pea was significantly greater than canola grown in monoculture with no N treatment (Kruskal–Wallis Test, n1 = 11, n2 = 20, P = 0.0366), but was not significantly different from canola grown in monoculture with a N treatment (Kruskal–Wallis Test, n1 = 11, n2 = 20, P = 0.445) (Figure 11). Therefore, further investigation will need to be done in the greenhouse to determine how pea and canola are interacting in order to confirm these results were the results of N nutrient transfer between plants.
Sources
Fletcher AL, Kirkegaard JA, Peoples MB, Robertson MJ, Whish J, Swan AD. Prospects to utilise intercrops and crop variety mixtures in mechanised, rain-fed, temperate cropping systems. In: Crop and Pasture Science, Vol. 67. CSIRO (2016). p. 1252–67. doi: 10.1071/CP16211
Mohapatra C, Chand R, Tiwari JK, Singh AK. Effect of heat stress during flowering and pod formation in pea (Pisum sativum physiology L). Mol Biol Plants. (2020) 26:1119–25. doi: 10.1007/s12298-020-00803-4
Porter MJ, Pan WL, Schillinger WF, Madsen IJ, Sowers KE, Tao H. Winter canola response to soil and fertilizer nitrogen in semiarid mediterranean conditions. Agron J. (2020) 112:801–14. doi: 10.1002/agj2.20119
Research Outcomes
Education and Outreach
Participation Summary:
A journal article was published on June 30th in Frontiers in Soil Science that detailed the findings of the microbial community analyses along with data collected by Dr. Isaac Madsen on crop yield, land equivalent ratio, and N response. This article can be found at the link and has the DOI: 10.3389/fsoil.2022.818862. Two separate conferences were also attended. One was WA SoilCon, where a recorded lightning talk was given on the findings for the microbial community analysis
Currently, there are plans to do public outreach events such as distributing informative brochures to local businesses in the Pullman-Moscow area. On top of this, an article is being wrote about Janice Parks by her undergraduate institution where some of the findings from this project, along with her recent publication, will be discussed in this article. This article will be sent to all subscribers to Pacific University's magazine/newsletter. There are also plans to publish a 'Timely Topic" with the WSU Wheat and Small Grains program on their website, to do a live interview on the “Wheat Beat” podcast hosted by Dr. Drew Lyon, to give a talk or poster at the Pacific Northwest Direct Seed Association annual conference and at a WSU Farmers Network or WOCS events, to publish an abstract to the WSU Dryland Field-day Abstract reference guide, to publish a reviewed WSU extension fact sheet and an article into Crops and Soils magazine, to work with the “Visualizing Microbial Agroecology” Western SARE PDP to create animations of the functional roles of the microbiome in peaola, to give two 40-minute webinar videos hosted on the famersnetwork.wsu.edu and broadcast via the css.wsu.edu/oilseeds, to present at the tri-societies, regional soil health and/or a like meeting, and publish manuscripts to journals such as Plant and Soil, Applied and Environmental Microbiology, and/or other like journals.