Progress report for GNE24-312
Project Information
Wheat is a staple crop globally and one of the five most valuable crops in Pennsylvania. Wheat is milled into flour to produce staple foods like bread and baked products. While studies have investigated the microbial diversity of wheat and bread products including sourdough starters, the changes of microbial communities from wheat to flour to sourdough starter remains less understood. I propose to investigate how wheat type and farming practices influence microbial communities across the farm-to-fermentation continuum using a sourdough model system. By tracking biodiversity changes from crop to food, I seek to better understand microbial dynamics in fermented foods to modernize and optimize fermented wheat-based products.
Ancient wheat species (einkorn, emmer, and spelt) and modern wheat varieties (hard red spring, hard red winter, and soft red winter) grown organically and conventionally in the Northeast region will be used in this study. 16S and ITS amplicon sequencing of wheat berries will determine diversity and abundance of bacteria and fungi. Whole wheat grain flours will be produced from the different wheat types and farming methods, then sequenced to track microbial changes during milling. Flours will be utilized to create sourdough starters, which will be monitored throughout fermentation (Day 1 to Day 10). Comparative physical and chemical analysis of starters will include organic acids, pH, microbial load, bubble production, and dough rise. Mature sourdoughs will be sequenced across 3 fermentation time points to track microbial changes from initial crop and flour communities to active starter phase. Outcomes from this study will provide insights into preservation and resilience of microbial communities from farm-to-fermentation and how farming practices may shape functional properties of fermented foods.
This proposed work aligns with our current USDA NIFA funded project (#2023-67017-40251) that characterizes sourdough microbiomes to enhance quality and nutrition of breads. The proposed study seeks to conserve biodiversity in locally produced staple crops, including ancient wheat species, which offer opportunities for market expansion and agricultural diversification. This research supports local farmers and consumers, promoting sustainable agriculture within Pennsylvania and the Northeast region.
Objective 1: Compare microbial diversity between wheat berries and whole wheat flour from ancient wheat species (T. monococcum: einkorn, T. dicoccum: emmer, and T. spelta: spelt) and modern wheat varieties (T. aestivum: hard red spring, hard red winter, and soft red winter) farmed organically and conventionally in the Northeast region. I hypothesize that farming practice will be the primary driver of microbial community differences, with organically farmed samples exhibiting higher alpha diversity than conventionally farmed samples. I also hypothesize that ancient and modern wheat types will share core microbial taxa but differ in relative abundances of specific bacterial and fungal taxa. Beta diversity is expected to differentiate samples primarily by farming practice, with secondary clustering by wheat type. This objective will result in tables with relative abundance of bacteria and fungi and results of statistical analyses of microbial diversity in wheat berries and flour samples.
Objective 2:
(2a) Track changes in microbial diversity from whole wheat flour through sourdough starter development (Day 1 to Day 10) using flours from ancient wheat species (einkorn, emmer, and spelt) and modern wheat varieties (hard red spring, hard red winter, and soft red winter) farmed organically and conventionally in the Northeast region.
(2b) Characterize sourdough starter functional outcomes. I hypothesize that microbial alpha diversity will decrease from flour to mature starter (Day 10) as fermentation selects for adapted taxa, primarily lactic acid bacteria and yeasts. I hypothesize that initial flour microbial composition will influence final starter microbial composition and measured chemical properties, with the bacteria-to-fungi identity and ratio correlating with pH and organic acid profiles. Starters from organically farmed wheat are expected to have distinct chemical profiles reflecting their original crop microbial communities. This objective will result in tables with relative abundance of bacteria and fungi across fermentation time points, statistical analyses of microbial diversity, microbial load, and proximate analysis measurements including pH and organic acid concentrations in active sourdough starters, and graphs illustrating relationships between microbial composition, wheat/farming types, and sourdough chemical properties.
The purpose of this project is to compare microbial communities between ancient and modern wheat grown using organic and conventional farming methods. This purpose is motivated by the question, "How do wheat varieties, production, and processing influence wheat biodiversity?”. To accomplish this, I will use our previously developed sourdough model system1,2. While previous studies have explored aspects of wheat cultivation and sourdough microbiome assembly independently, how different wheat varieties and farming practices impact biodiversity (defined as richness and abundance of microbial communities) from farm-to-fermentation is unknown.
To study biodiversity differences in wheat varieties, we will use amplicon sequencing of bacterial and fungal populations to compare modern, domesticated wheat (T. aestivum: hard red spring, hard red winter, and soft red winter) to ancient, domesticated wheat (T. monococcum: einkorn, T. dicoccum: emmer, and T. spelta: spelt). We will investigate how farming practices (organic vs. conventional) influence microbial biodiversity across both modern and ancient wheat types. Next, we will track how microbial communities change as wheat grain is milled into flour and then fermented into active sourdough starters. Comparative chemical analysis of sourdough starters (pH, organic acids) will be measured to determine how microbial composition influences functional properties. This study aims to understand biodiversity changes from farm-to-fermentation (grain-to-gain) and how microbial communities shape the properties of the fermented food product.
Wheat, a staple crop worldwide, plays a significant role in global nutrition. Wheat is one of the three main cereal cash crops in the world, providing more food energy than any other crop3. Wheat stands among the top 5 most valuable crops cultivated in Pennsylvania, with a focus on winter wheat4. Common wheat, known as T. aestivum, is the most widely cultivated wheat species in the world3. Ancient grains have had a resurgence of interest due to consumer demand for functional foods. For example, individuals affected by gluten intolerances who need to avoid gluten have found alternatives in ancient grain products3. Einkorn (T. monococcum) and emmer (T. dicoccum) are ancient wheat species and two of the three founder crops in the development of agriculture; however, they are not widely used anymore3. Recent regional evaluations of ancient grains have shown them to be more adaptable, genetically diverse, and require less fertilizer input than wheat10. Ancient grains can also offer a better nutritional composition than wheat and has potential for market growth in food. After wheat has been grown and harvested, the grain can then be milled to leave the endosperm, producing white flour, or used in whole (endosperm, bran and germ), producing whole grain flour. When refined by removing the bran and germ, the endosperm is mostly carbohydrate and lacks nutrients3,5.
Wheat flour is used to produce staple baked goods including bread. Commercial bread relies on a single yeast strain, Saccharomyces cerevisiae, to leaven flour and water. In contrast, sourdough relies on naturally occurring yeast and bacteria commonly found in wheat flour, water, and the environment. These microorganisms, known as the ‘sourdough microbiome’, function to ferment flour and water. Similar to interest in ancient wheat, there has been a resurgence in sourdough due to its superior taste, perceived health benefits, and suitability for individuals with gluten intolerances6.
Microbial communities in sourdough assemble from the interaction between ingredients (type of flour, water, salt) and technological parameters (temperature, dough hydration, dough yield, fermentation time, percent sourdough inoculum, etc.)7. Microbial communities in cereal grains depend on factors including climatic conditions (temperature and rainfall), insects or fungal contamination and agricultural practices (tillage, use of insecticides and fungicides)7. Moving from crop microbiota to flour microbiota, previous studies have demonstrated that type of flour plays a limited role in shaping diversity and abundance of microbial communities. However, the use of white flour or whole-grain flour shows significant differences, affecting the assembly of sourdough microbiota. The microbiome of raw ingredients such as flour can impact sourdough quality. For example, microbes from flour produce organic acids and other metabolites that affect the sensory, technological, safety, nutritional and functional properties of cereal based foods, including bread.
This research is significant for Pennsylvania farmers as it could provide insights into how ancient wheat and farming practices can be leveraged to enhance the quality of grain-based products. In addition, this study could boost the organic market and ancient grain market in the state. In 2023, winter wheat production in Pennsylvania amounted to $1.2 million, yielding 17.5 million bushels9. By examining microbial biodiversity from wheat to sourdough formation, this study aims to enhance our understanding of microbial preservation in farm-to-fork systems. This knowledge can support sustainable organic farming practices and the production of ancient wheat varieties, meeting consumer preferences for modernized grain-based products. This research benefits Pennsylvania's agricultural landscape by equipping farmers with insights to cultivate new wheat varieties for enhanced food products10.
Research
Wheat Sample Collection and Processing:
Ancient (T. monococcum: einkorn, T. dicoccum: emmer, and T. spelta: spelt) and common wheat varieties (T. aestivum: hard red spring, hard red winter, and soft red winter) were obtained from an on-going collaboration with the PSU Small Grain Variety Testing Program (see letter from Carrijo) and Northeast region growers and millers. Winter wheat varieties are planted in fall (late September to early October) and harvested in spring, while spring wheat varieties are planted in late spring (late April to early May) and harvested in late summer. At harvest, wheat berries are removed from the husk. Each wheat berry contains the bran, germ, and endosperm, which together are ground into whole grain flour.
Twelve grain samples representing different combinations of wheat type and farming practice were collected. Organic samples were obtained from River Valley Community Grains, including hard red spring (PA), hard red winter (NJ), einkorn (PA), emmer (PA), and spelt (PA). Conventionally farmed ancient grains, einkorn (PA), emmer (PA), and spelt (PA), were obtained from Castle Valley Milling. Conventionally farmed hard red spring (MA) and hard red winter (MA) were obtained from Ground Up Grain. Conventionally farmed soft red winter wheat (FS746) was obtained from a previous trial through the PSU Small Grain Variety Testing Program. Approximately 1-5 pounds of wheat berries were collected per sample and stored at room temperature until processing.
Sample nomenclature is as follows: AOEi (ancient, organic, einkorn), ACEi (ancient, conventional, einkorn), AOEm (ancient, organic, emmer), ACEm (ancient, conventional, emmer), AOSp (ancient, organic, spelt), ACSp (ancient, conventional, spelt), COHS (common, organic, hard red spring), CCHS (common, conventional, hard red spring), COHW (common, organic, hard red winter), CCHW (common, conventional, hard red winter), CCSW1 (common, conventional, soft red winter, replicate 1), and CCSW2 (common, conventional, soft red winter, replicate 2).
The conventional soft red winter wheat (FS746) represents standard Pennsylvania wheat farming practices, including spring herbicide and fungicide applications, split nitrogen fertilizer applications, and a plant growth regulator at heading. Organic farming practices followed USDA organic certification standards, excluding synthetic pesticides, herbicides, and fertilizers.
Flour Milling:
All 12 grain samples were milled into whole grain flour in biological quadruplicate (n=48 total flour samples). Wheat berries were ground using a coffee grinder with a 4-blade attachment for 60 seconds total in 10-second intervals to prevent overheating. Milled flour was transferred to sterile bags and stored at room temperature until use. Particle size distribution and moisture content will be measured for all flour samples to ensure milling consistency and characterize physical properties across wheat types and farming practices.
Starter Preparation:
Sourdough starters were propagated to active phase through 10 days of daily feeding. On Day 0, 12 g of milled flour was combined with 12 g of sterile deionized water in a loosely capped 50 mL conical tube and mixed with a sterile spatula. Every 24 hours for 10 days, 16 g of starter was removed and discarded, and the remaining starter was fed 8 g of flour and 8 g of sterile deionized water, maintaining a 1:1:1 ratio of starter:flour:water typical for sourdough maintenance. Active phase, typically reached between days 7-14, is characterized by the starter doubling in size with visible bubble production. Dough rise was measured using a ruler to track vertical rise in millimeters, and bubble production was qualitatively assessed every 24 hours before feeding using a three-point scale (low, medium, high). All discarded starter material was collected and stored at -20°C for proximate analysis and -80°C for DNA extraction. Fermentation temperature was monitored continuously with a HOBO temperature logger (Onset), with all starters maintained at room temperature (approximately 20-22°C).
Microbial Load:
Microbial load was quantified in wheat berries, flours, and Day 10 starters to determine total microbial counts and the relative ratio of bacteria to fungi. One gram of the sample was aseptically weighed into a sterile 15 mL conical tube containing 9 mL of autoclaved deionized water and vortexed vigorously for 30 seconds. Serial dilutions were prepared by transferring 1 mL to 9 mL autoclaved deionized water. Dilutions were plated onto aerobic plate count (APC) and yeast and mold (YM) Petri Films. APC plates were incubated at 35°C for 24 hours, and YM plates at 25°C for 1-3 days. Plates containing 25-250 colonies were counted, with all red colonies with gas bubbles counted for APC. For YM plates, yeasts appeared as small blue-green colonies with sharp edges, while molds appeared as large colonies with fuzzy edges. Colony forming units per gram (CFU/g) were calculated as: CFU/g = (average colony count from duplicates) × (dilution factor) × (1/volume plated).
DNA Extraction and Sequencing:
DNA was extracted from all 12 grain types in biological quadruplicate at three stages: wheat berries (n=48), flours (n=48), and sourdough starters at Days 0, 5, and 10 (n=144), for a total of 240 samples. DNA extraction was performed using the QIAGEN PowerSoil Pro Kit following the manufacturer's protocol. The V4 region of the bacterial 16S rRNA gene was amplified using primers 515F and 806R, and the fungal ITS1 region was amplified using primers ITS1f and ITS22. PCR amplification included optimization of cycle number (25, 30, or 35 cycles) determined through pilot studies. Each PCR reaction included negative controls (no template) and positive controls. DNA Extraction, PCR, and library preparation was conducted at the Collaboratory facility at Penn State with barcoded primers for multiplexed sequencing. Sequencing was performed using the Illumina NextSeq P1 platform at the Genomics CORE Facility at Penn State, targeting approximately 10 GB of sequence data per sample.
pH:
pH was measured in all flour samples and starters at Days 0, 5, and 10. One gram of sample was weighed into a 50 mL conical tube and mixed with 10 mL of deionized water (1:10 dilution). The mixture was vortexed vigorously for 30 seconds and allowed to stand for 5-10 minutes for flour or 2-5 minutes for starter to allow hydration and settling. Immediately before measurement, samples were briefly vortexed (5-10 seconds) to resuspend. A calibrated pH probe (FiveEasy Plus FP20 pH meter, Mettler-Toledo). Three technical replicates were performed for each sample.
Organic acids:
Lactic and acetic acid, the predominant organic acids in sourdough, were quantified in starters at Days 0, 5, and 10 (n=144 samples total). Organic acids were extracted following a previously established method16. Briefly, 1 g of starter was combined with 10 mL of sterile deionized water (1:10 dilution) in a 15 mL conical tube and vortexed for 5 seconds. Tubes were shaken on a plate shaker at 120 RPM for 1 hour with tubes positioned on their side to maximize mixing. Samples were then centrifuged at 8,000 × g for 15 minutes at room temperature. The supernatant was filtered through a 0.22 μm syringe filter into a new labeled tube and stored at 4°C until analysis.
Standard curves were prepared using lactic acid (VWR) and acetic acid (Millipore Sigma) at concentrations of 3.125, 6.25, 12.5, 25, 50, and 100 mM. Butyric acid (50 mM) was used as an internal standard. Standards and samples were prepared by mixing 500 μL of standard or filtered sample with 500 μL of internal standard solution (10 mM H₂SO₄ + 100 mM butyric acid) in HPLC vials, yielding a final internal standard concentration of 5 mM H₂SO₄ and 50 mM butyric acid.
Organic acid analysis was performed using a Thermo Scientific Dionex ICS-5000+ high performance liquid chromatography (HPLC) system equipped with an Aminex HPX-87H ion exclusion column (300 × 7.8 mm; Bio-Rad Laboratories) maintained at 50°C. The mobile phase consisted of 5 mM H₂SO₄ at a flow rate of 0.45 mL/min with an injection volume of 25 μL. Lactic and acetic acid were identified based on retention time and quantified by comparing peak areas to standard curves. The fermentation quotient (FQ) was calculated as the molar ratio of lactic acid to acetic acid16.
Bioinformatics:
Raw bacterial 16S rRNA and fungal ITS sequence reads will be processed using the DADA2 pipeline (v1.20 or higher) in R (v4.0 or higher) following established protocols for amplicon sequence variant (ASV) inference13. Low-quality sequence reads were filtered, low-quality bases will be trimmed, calculation of error rates will be performed, and ASVs will be deduced from the remaining sequence reads. Paired-end sequence variants will be merged into contigs where contigs <251 bp or >253 bp will be removed. Chimeras will be detected and removed, and remaining ASVs will be assigned taxonomy using the Silva database v13214 for bacteria and the UNITE database15 for fungi. ASVs assigned to mitochondria or chloroplasts will be filtered out from the bacterial taxa and reads unassigned at the phylum or class levels.
Statistics & Visualization:
Statistical analyses will be conducted to address each hypothesis, comparing microbial communities across farming practices (organic vs. conventional), wheat types (ancient vs. modern), and processing stages (wheat berry, flour, and starter at Days 0, 5, and 10).
Alpha Diversity: Microbial alpha diversity will be assessed using Shannon diversity index (accounting for richness and evenness) and Chao1 richness estimator. Alpha diversity metrics will be compared between groups using one-way analysis of variance (ANOVA) or Kruskal-Wallis tests, depending on data distribution, followed by Tukey's honestly significant difference (HSD) or Dunn's post hoc tests for pairwise comparisons. Linear mixed-effects models may be employed to account for repeated measures across fermentation time points.
Beta Diversity: Microbial community composition (beta diversity) will be assessed using Bray-Curtis dissimilarity matrices calculated from rarefied ASV count tables. Principal coordinate analysis (PCoA) ordination plots will be generated to visualize inter-sample distances for bacterial and fungal communities separately. Permutational multivariate analysis of variance (PERMANOVA) will be used to test for significant differences in community composition between farming practices, wheat types, and processing stages. Hierarchical clustering analysis (HCA) based on Bray-Curtis dissimilarities will be performed using Ward's minimum variance method to identify sample clustering patterns.
Differential Abundance: Linear discriminant analysis effect size (LEfSe) or DESeq2 will be used to identify bacterial and fungal taxa that are differentially abundant between groups (e.g., organic vs. conventional, ancient vs. modern, wheat berry vs. flour vs. starter).
Correlation Between Microbial Communities and Chemical Properties: Relationships between microbial composition and sourdough chemical properties (pH, lactic acid, acetic acid, fermentation quotient) will be assessed using Pearson or Spearman correlation analyses. Shannon diversity and dominant taxa at the genus level will be correlated with chemical measurements. Correlations will be visualized using the corrplot package in R. Principal component analysis (PCA) will be performed on chemical properties to identify overarching patterns, and HCA will be used to cluster samples based on Euclidean distances of standardized chemical data using Ward's linkage.
Microbial Load Analysis: CFU/g counts from APC and YM plating will be log-transformed and compared across wheat types, farming practices, and processing stages using ANOVA with Tukey's HSD post hoc tests. The bacteria-to-fungi ratio will be calculated and correlated with chemical properties and alpha diversity metrics.
All visualizations will be generated using the ggplot2 package in R. Taxonomic composition bar plots will display the relative abundance of bacterial and fungal taxa (phylum, family, or genus level) above 1% relative abundance across wheat types, farming practices, and processing stages. PCoA ordination plots will be colored by relevant metadata (farming practice, wheat type, processing stage) to visualize clustering patterns. Heatmaps will be generated to display correlations between microbial taxa and chemical properties. Box plots and violin plots will be used to compare alpha diversity metrics, microbial load, and chemical properties across groups. All figures will include appropriate statistical annotations (e.g., significance levels from post hoc tests).
Sample Acquisition and Preparation: All wheat samples required for the study have been successfully collected and obtained, including ancient wheat species (einkorn, emmer, and spelt) and modern wheat varieties (hard red spring, hard red winter, and soft red winter) from both organic and conventional farming systems across the Northeast region. Major accomplishment from previous reporting period: Twelve distinct wheat types have been sourced, collected, and will be analyzed across multiple processing stages (grain, flour, and sourdough starter).
Method Development and Optimization: Major accomplishments from previous reporting period include three key pilot studies outlined below that we necessary for optimizing protocols for the full-scale study:
Sourdough Starter Propagation (Objective 2): A pilot study comparing four hydration levels (50%, 60%, 70%, and 80%) for whole wheat flour sourdough starter propagation determined that 50% hydration is optimal for the wheat varieties being studied. Determining hydration levels is important because different flours types can require higher hydration for a successful sourdough starter. This study also identified that ambient benchtop incubation is preferable to controlled temperature in an incubator to minimize contamination. Finally, a 4-blade coffee grinder attachment produces more consistent flour particle size compared to the 3-blade attachment. These findings have been incorporated into the standardized protocol for the main study.
Microbial Load Quantification: A comprehensive plating pilot study is on-going to determine optimal dilution ranges for enumerating bacteria and fungi from wheat berries, flour, and Day 10 sourdough starters (Objective 1). This study tests dilution series from 10-1 to 10-8 across all three sample types using both 3M Petrifilm Yeast & Mold plates and Standard Plate Count Agar to ensure countable colonies (25-250 CFU) in the full study. Preliminary results will inform the final dilution scheme for all 240 samples. Microbial load data is critical for determining bacteria to fungi ratios in the starters outlined in Objective 2.
DNA Extraction and PCR Optimization: The objective of this study is to optimize DNA extraction and PCR amplification conditions for bacterial (16S rRNA V4 region) and fungal (ITS1 region) communities. This study will test three PCR amplification cycles (25, 30, and 35 cycles) for DNA templates obtained from grain, flour, and starter samples to achieve optimal amplification while minimizing over-amplification artifacts such as primer dimers. The QIAGEN PowerSoil Pro Kit has been selected for DNA extraction, and both negative and positive controls (ZymoBIOMICS for bacteria, ATCC Mycobiome for fungi) have been identified to ensure protocol robustness. Results from gel electrophoresis will determine the optimal cycle number for library preparation prior to sequencing to achieve objective 1 and 2 outcomes.
Protocol Refinements: Throughout these pilot studies, several methodological improvements have been identified and implemented, including standardized milling procedures (60-second total milling time in 10-second intervals), the addition of microbial load quantification, and refined starter propagation methods and feeding ratios (1:1:1 starter:flour:water). These refinements ensure consistency and reproducibility across all 12 wheat types and their biological replicates.
Next Steps: Upon completion of the ongoing pilot studies, full-scale sample processing will begin, including milling of all 48 flour samples, propagation of 48 sourdough starters through 10 days of fermentation, and collection of samples for microbial load quantification, pH measurement, organic acid analysis, and DNA extraction (February-April). Sequencing of all 240 samples (48 grains + 48 flours + 144 starters across three time points) is planned for April. By the end of April, all data for the project should be collected and ready to analyze.
Education & outreach activities and participation summary
Participation summary:
Throughout my academic career, I have been committed to education and outreach in food science and agriculture. I have led and participated in workshops and short courses focused on food microbiology, food mycology, international agriculture, and food security. For the past three summers, I have served as a mentor to undergraduate students participating in the USDA REEU (Research and Extension Experience for Undergraduates) and PSU SROP (Student Research Opportunities Program). In this role, I guided students in developing independent research projects, assisted with experimental design and troubleshooting, and helped them effectively communicate their scientific findings through presentations and written reports.
To share findings from this research with current and future scientists, as well as the broader community, I have developed and delivered interactive sourdough microbiology workshops. To date, I have completed two workshops at Penn State, each with 10-15 participants including undergraduate and graduate students, faculty, and community members. A third, larger-scale workshop is planned for early May 2026 at the One Health Microbiome Symposium, and the workshop will reach approximately 100 attendees from Penn State, institutions across the nation, and potentially international participants.
These workshops consist of two components:
(1) Educational Presentation (20-30 minutes): I provide an overview of my research investigating sourdough microbiomes and how farming practices and wheat types influence microbial communities from crop to food. The presentation covers:
- The background of sourdough microbiomes, how they establish, and why they are important
- How processing steps (harvesting, milling, fermentation) can shape microbial communities
- The critical role of microbial diversity in determining sourdough starter characteristics and bread quality
- The broader implications of microbial diversity for food systems and human nutrition
(2) Hands-On Activity (30-40 minutes): Participants engage in sensory evaluation and observation of sourdough starters and breads made from distinct microbial communities. Students observe active sourdough starters, compare bubble production, rise, and smell, and then taste the breads made with different starters to experience firsthand how microbial communities influence functional outcomes such as flavor, texture, and acidity. This experiential component helps participants understand the connection between "unseen" microorganisms and tangible food quality attributes.
At the conclusion of each workshop, participants receive a sourdough starter culture to take home, along with a detailed care guide including feeding schedules, troubleshooting tips, and information about sourdough fermentation. This takeaway encourages continued engagement with the science of food fermentation beyond the workshop. There is also a “Sourdough Healthline” GroupMe offered to students to send pictures of their creations and ask for help troubleshooting as they bake breads at home.
Participants in the initial two workshops reported increased understanding of the role of microbes in food systems, and several expressed interest in exploring using different grain varieties in their own baking. The hands-on nature of the workshop has been particularly effective in making microbiology concepts accessible to diverse audiences, including those without scientific backgrounds.
To extend the reach of this research beyond in-person workshops, I plan to develop an educational video presentation for dissemination through multiple channels. This resource will:
- Summarize the study's objectives, methods, and key findings regarding microbial communities in ancient and modern wheat varieties under organic and conventional farming practices
- Explain how wheat variety selection and farming practices influence microbial biodiversity from grain through flour to sourdough starter
- Discuss the potential benefits of incorporating organic farming systems and ancient wheat varieties to enhance microbial resilience and improve outcomes in grain-based food products
- Provide practical recommendations for farmers and bakers interested in sourdough fermentation with different wheat varieties from different farming practices
The video will be provided as a QR code on a given pamphlet at the May 2025 conference and made available through [Global Ag Network/Penn State Extension/other relevant platforms] to reach diverse audiences including Northeast region farmers, millers, bakers, agronomists, agricultural researchers, Extension educators, and food industry professionals. By making the presentation freely accessible online, we aim to support education on microbial biodiversity from crop to food.