# Source paper archive - Source title: Effects of Reduced Watering Regimes on Hops Growth and Rhizospheric Microbial Communities - Author(s): Alexandra McElwee-Adame, Caden Mulligan, Kailyn Kiburi, Zoe Glynn, Mani Golshani, Ahmed Alkhafaji, George Vourlitis, Arun Sethuraman - Source landing URL: https://www.biorxiv.org/content/10.64898/2026.11.03.764026v1 - PDF URL: https://www.biorxiv.org/content/10.64898/2026.11.03.764026v1.full.pdf - PDF SHA-256: 819222cd6afc37469ebf5826d39911280adec9ecdd9c57f46682660f2e9e21de - Converter: pymupdf4llm 0.0.27 - Limits: Automated PDF text extraction; reading order, equations, tables and formatting may be incomplete or incorrect. Images are omitted, and no OCR was performed. Markup and links are neutralized for safe downloading. Consult the original PDF for the authoritative version. --- [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 1 **Effects of Reduced Watering Regimes on Hops Growth and Rhizospheric Microbial** 2 **Communities** 3 4 Alexandra McElwee-Adame [1, 2], Caden Mulligan [1], Kailyn Kiburi [1], Zoe Glynn [1], Mani Golshani [1], 5 Ahmed Alkhafaji [1], George Vourlitis [3], Arun Sethuraman [1] 6 7 1 Department of Biology, San Diego State University, 5500 Campanile Dr, San Diego, CA 92182 8 2 Department of Evolution, Ecology, and Organismal Biology, University of California- Riverside, 9 900 University Ave, Riverside, CA 92521 10 3 Department of Biological Sciences, California State University, San Marcos, 333 S Twin Oaks 11 Valley Rd, San Marcos, CA 92096 12 13 **Emails:** 14 Alexandra Mcelwee-Adame: anmcelweeadame@sdsu.edu 15 Caden Mulligan: cademulligan@gmail.com 16 Kailyn Kiburi: kai.kiburi@gmail.com 17 Zoe Glynn: zoesmithglynn@gmail.com 18 Mani Golshani: manigolshani8703@gmail.com 19 Ahmed Alkhafaji: Ahmedwamalk@gmail.com 20 George Vourlitis: georgev@csusm.edu 21 Arun Sethuraman: asethuraman@sdsu.edu 22 23 **Corresponding author** : Arun Sethuraman (asethuraman@sdsu.edu) 24 25 **Date of Submission:** November 3, 2026 26 27 **Word Count:** 11,482 28 29 **Number of Tables and Figures:** Tables (17), Figures (8) 30 31 **Keywords:** Hops, _Humulus lupulus_, drought tolerance, common garden, rhizosphere, crops 32 33 **Highlight:** 34 Impacts of reduced watering regimes varied significantly between cultivar types, revealing 35 patterns in drought tolerance amongst cultivars with shared recent introgression events with 36 USDA breeding wild male Utah 524 and 526. 37 38 39 40 41 42 43 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 44 **Abstract** 45 Marked by shifts in historic climatic patterns, the effects of climate change have begun to disrupt 46 biological life worldwide. In agricultural systems, the abiotic effects of climate change such as 47 increased temperatures and reduced rainfall extend beyond crop yield and can be felt at the 48 ecological level through microbial community shifts associated at the interface of the root/soil 49 matrix, known as the rhizosphere. Shifts in rhizospheric community composition have been 50 known to greatly impact crop health, making common garden studies examining the totality of 51 climate related impacts on crops of the utmost importance. Using a combination of both 52 phenotypic and metabarcoding data collected from the rhizosphere, we present a multi-month 53 drought tolerance experiment using the cash crop _Humulus lupulus_ (hops) and five of its 54 cultivars; Cascade, Centennial, Chinook, Comet and CTZ. Statistical analyses of phenotypic 55 data revealed that hops displayed a differential impact on reduced watering strategies while 56 rhizospheric data revealed more consistent patterns of association between microbes and 57 cultivar type. We hope results from our study can be used to further expand our knowledge on 58 the impact of future climate change and necessary reduced watering strategies on agricultural 59 systems, providing insight into potential mechanisms of response for drought tolerance in crop 60 systems. 61 62 63 64 65 66 67 68 69 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 70 **Introduction** : 71 Climate change has become one of the primary factors affecting agricultural production 72 worldwide. Prior to the turn of the 21st century, research suggested that global temperatures 73 would increase at a rate of nearly one third of a Fahrenheit degree each decade (Vose et al., 74 2021). However, studies in recent years have found that this increase in global temperatures is 75 more likely to reach 1.5 °C or beyond by 2050 (Eftekhari, 2022), resulting in drastic alterations 76 to global climate patterns such as changes in the frequency and intensity of extreme weather 77 events such as floods and heatwaves, irregular precipitation patterns, and reduced snowpack 78 leading to decreased water availability during growing seasons (Arora, 2019; Malhi et al., 2021). 79 At the organismal level, these abiotic stressors can cause damage to crop physiology by either 80 physically damaging the crop itself, therefore directly reducing product yield (Bibi & Rahman, 81 2023; Yuan et al., 2024) or by altering the rhizospheric microbiome and biogeochemistry of the 82 surrounding soil (Adebayo et al., 2026). 83 Many crop species are known to harbor beneficial microbes known as plant-growth-promoting 84 microorganisms (PGPM) that assist in nutrient absorption key for proper growth and 85 development as well as water-use efficiency. Disturbances to the plant-surrounding microbiome 86 can result in shifts in the composition and abundance of the microbial community (Laishram et 87 al., 2024; Muhammad et al., 2025). At the cellular level, microbes classified as PGPM’s have 88 been found to accumulate a surplus of vital compounds such as amino acids and sugars, which 89 are necessary to withstand degradation caused by unfavorable or infrequent watering conditions 90 (Jha & Saraf, 2015). Bacterial genera such as _Bacillus_ have been of high interest to crop 91 breeders due to their associations being marked with an increase in resistance to drought in 92 several major crops, such as maize (Vardharajula et al., 2011), peppers (Lim & Kim, 2013), 93 soybean (Sheteiwy et al., 2021), wheat (Rashid et al., 2022; Sood et al., 2020), chickpea 94 (Kumar et al., 2016) and cannabis (Wiszpolski & Stolarski, 2026). Therefore, in order for plant [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 95 breeders to properly prepare and develop new and more resilient cultivated crop varieties in the 96 future, a holistic approach is needed to understand the effects of abiotic stress on the totality of 97 the plant itself and reveal underlying mechanisms that may be involved in abiotic stress 98 tolerance. 99 For crops produced in the mid- and high-latitude ranges, ideal growing ranges in future years 100 will need to expand northward (King et al., 2018); however, for some farmers, especially those 101 in northern regions like the Pacific Northwest of the United States, this may not be an option. 102 Changes in precipitation regimes in particular are expected to strongly impact farmers at 103 regional levels, resulting in inconsistent water availability (Feng et al., 2019; Konapala et al., 104 2020). As a result, farmers will need to turn to selective plant breeding strategies to address 105 consequences associated with climate change in these regions. 106 Hops ( _Humulus lupulus_ ) is a dioecious perennial cash crop that is natively distributed 107 throughout the Eurasian and North American continents; however, through breeding program 108 efforts, hops can now be produced globally (Murakami et al., 2006). Hops display an ideal 109 growing range between 45° and 50° north of the equator but have become locally adapted to 110 various regions outside of this range via selective breeding efforts (Castro et al., 2021; Ruggeri 111 et al., 2024). Commercially, over 250 cultivars (cultivated varieties) have been developed 112 worldwide, each displaying differences in bitter acid and essential oil compositions, key 113 benchmarks in the brewing industry (McElwee-Adame et al., 2025), as well as differences in 114 agronomic traits such as increased growth, yield, seasonality, and disease/pest tolerance 115 (McAdam et al., 2013). 116 Majority of the global hops production is dominated by two countries: Germany and the United 117 States (Agraso-Otero et al., 2026). Unfortunately, in recent years, US production has seen a 118 decline in acres of hops harvested and value of production, with the 2023 growing year [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 119 recording a devastating 21% decline in overall production value compared to the previous 120 growing year (USDA-SASS National Hops Report, 2024). Coincidentally, in recent years, the 121 Pacific Northwest (PNW) has seen extreme changes in climatic regimes, with increased 122 summer temperatures and drought events becoming more common across the region. 123 Previous studies utilizing simulated data have found that higher global temperatures are also 124 expected to decrease overall hops production and alter bitter acid production and essential oil 125 compositions (Mozny et al., 2009, 2023). Of the total hops produced worldwide, an 126 overwhelming majority (~98%) is used by the brewing industry (Korpelainen & Pietiläinen, 127 2021), valued at 113 billion dollars (US) in the United States beer market alone (Brewers 128 Association, 2025), meaning that alterations to future hops products will directly alter future beer 129 production. Additionally, previous studies aiming to generate real-time data using common 130 garden experiments have already begun to assess the impact that reduced watering regimes 131 and increased temperatures may have on hops growth and production (Eriksen et al., 2020; 132 Nakawuka et al., 2017). However, both these studies evaluated the impact of abiotic stress on 133 hops solely from a physiological standpoint, completely negating belowground associations. 134 Here we present a multi-month common garden experiment addressing the impact of reduced 135 watering regime strategies on hop growth and rhizospheric microbial associations. By 136 employing a combination of genomic and phenotypic data, we address three main questions: 1) 137 Which of the selected cultivars is least impacted by drought, therefore making it an ideal 138 candidate for future breeding program efforts? 2) Are there any patterns between hops’ 139 resilience under reduced watering strategies and pedigree? 3) Could belowground associations 140 such as rhizospheric microbial and fungal compositions confer drought tolerance? We 141 hypothesize (1) that cultivars that share a more recent hybridization event with wild American 142 hops will display a higher tolerance for drought via adaptive introgression, (2) that this pattern of 143 tolerance will be shared amongst individuals of similar pedigree history, and (3) elevated [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 144 community composition of known beneficial microbes coevolved in response to local adaptation 145 to North American soils. 146 **Methods:** 147 _Cultivar Selection and Pre-Drought Regime Care_ 148 Five cultivated varieties were selected based on the 2023 National Hops Report based on two 149 criteria: top-producing cultivars during the 2023 growing season and access to rhizomes. The 150 following cultivars were then selected: Cascade (USDA Accession #: 56013), Centennial USDA 151 Accession #: 21507), Chinook (USDA Accession #: 21226), Comet (USDA Accession #: 62013), 152 and Columbus/ Tomahawk/ Zeus (hereon, CTZ). In April 2024, 150 rhizomes per cultivar (total 153 n=750) were purchased from The Thyme Garden nursery (Alesca, Oregon) and shipped to San 154 Diego State University (SDSU), stored at 4C until transport to maintain dormancy. Rhizomes 155 were then transported from SDSU to the California State University San Marcos (CSUSM) 156 Biology Department Greenhouse (33.131294, -117.156819) and planted roughly two inches 157 below the soil line into one gallon nursery pots with potting soil composed of aged forest 158 product, sphagnum peat moss, perlite and 0.3-0.3-0.5 NPK fertilizer derived from earthworm 159 castings, bat guano, oyster shell, and dolomite lime (FoxFarm Soil & Fertilizer Co 2026). No 160 additional amendments or fertilizers were added to the experimental pots for the remainder of 161 the experiment. 162 From April 2024 to June 2025, juvenile plants were reared at the CSUSM greenhouse. To 163 minimize the risk of root rot amongst our juvenile individuals, plants were watered twice a week 164 via manual irrigation from a hose, achieving roughly 1 inch of water within each 1-gallon pot per 165 watering session from April 2024 to October 2024, and then once a week from October 2024 to 166 the end of June 2025. At the end of June 2025, 90 individuals per cultivar were randomly 167 selected (n=450) and assigned one of three treatment regimes: a control treatment (100% 168 recommended watering), moderate drought (75% recommended watering), and severe drought [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 169 (50% recommended watering). Plants were then transferred to SDSU’s Alvarado Greenhouse 170 (32.7771272, -117.0629152) for the twelve-week drought experimental period (7/1/25-9/23/25). 171 Due to low germination success with our Chinook and Comet rhizomes, 90 Chinook and 60 172 Comet juvenile plants were purchased from Great Lake Hops (Ottawa County, MI) and used for 173 the experimental period. 174 _Drought Regime Delivery and Experimental Design_ 175 To standardize delivery of water regimes, we constructed three drip irrigation systems (one per 176 treatment) and utilized a timer system for regime delivery. Individuals of the same treatment 177 type (n=30 per cultivar type; n= 150) were randomly placed along a greenhouse bench and 178 were attached to one of three irrigation systems: control, moderate, or severe. For our 179 experiment, we opted to establish our control conditions as receiving 1.5 inches of water per 180 week. We measured the volume necessary to create 1.5 inches of water within our nursery 181 pots, which equated to roughly 850 mL/week of water for our control treatment. From there, we 182 calculated that our moderate regime individuals would receive 600mL/week (75% 183 recommendation) and the severe regime would receive 425mL/week (50% recommendation). 184 To avoid oversaturating the root system of our hop plants, we opted to split weekly waterings 185 into two disbursements per week, resulting in individuals receiving either 425 mL (control), 300 186 mL (moderate), and 215 mL (severe) per disbursement. To ensure that each individual within 187 each treatment type received the same amount of water, we timed each treatment system to 188 estimate the amount of time it took to achieve each of our targeted volumes (425 mL, 300 mL, 189 and 215 mL) for each disbursement, resulting in about 7 minutes of watering for our control 190 treatment, 5 minutes for moderate, and 3 minutes for severe treatment. Randomized spot 191 checks were performed throughout the duration of the experiment to monitor for consistency of 192 water delivery along irrigation lines. [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 193 Daily environmental conditions such as hourly average temperature and relative humidity were 194 recorded and monitored via HOBO data loggers (Onset Computer Corporation, Bourne, MA). 195 During the 12-week drought period, plants received an average of 12-14 hours of daylight per 196 day. Additionally, plants during the experimental period were maintained and cared for through 197 weekly maintenance such as trimming dead shoots or vines to promote growth and removal of 198 agricultural pests such as spider mites via predation by the biocontrol agent _Hippodamia_ 199 _convergens_ (via NaturesGoodGuys, Amazon.com) and were released twice per growing 200 season. 201 _Phenotypic Data Collection_ 202 To measure each cultivar's growth rate response to varying levels of drought, weekly vertical 203 height was measured for each individual, taken from the base of the plant along the soil line to 204 the tip of the apical shoot. In the event that the apical shoot was not available (i.e., died off due 205 to heat stress), we would then use the next available lateral shoot to serve as a proxy apical 206 shoot for measurements. Vertical growth measurements were recorded weekly over the twelve 207 week growing season. At the end of the twelve-week growing season, weekly growth rates were 208 calculated from our data using the following equation: h t+1 - h t where t is the week of interest and 209 h is the vertical height for that particular week. Additionally, at the end of the experimental 210 period, aboveground biomass was collected for each individual by trimming plants down to the 211 base of the soil line, bagged, and weight recorded. Leaf samples for each individual were then 212 bagged and stored in -80C freezer for downstream analyses. 213 214 _Difference in Biomass Production and Average Growth Rate (AGR)_ 215 To test hypotheses of the impacts of cultivars or watering regimes on weekly growth rates or 216 biomass production, an ANOVA was performed using the base R function aov() (R Core Team). 217 For these ANOVA analyses, treatment type, cultivar type and week were treated as the [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 218 independent variables while weekly growth rates and biomass were treated as the dependent or 219 response variables. First, we assessed each watering regime dataset (control, moderate, and 220 severe) for normality and homoscedasticity using the base R functions shapiro.test() and 221 bartlett.test(). If both assumptions were met, a Tukey Post Hoc test was performed on 222 statistically significant datasets to identify significant pairwise differences using the base R 223 function TukeyHSD(). If a dataset failed to meet normality, the R package best.normalized 224 (Peterson & Cavanaugh, 2020) was used to determine and perform the optimal data 225 transformation for either biomass or weekly growth rate measurements. Tests for normality and 226 homoscedasticity were then repeated, and if assumptions were met, a Tukey Post Hoc test was 227 performed as described above. Significance was assessed using the threshold adjusted p-value 228 < 0.05, assuming a null hypothesis of no significant difference in biomass between cultivar or 229 treatment types. If datasets failed ANOVA assumptions, we opted to use a Kruskal-Wallis test 230 using the kruskal.test() base R function followed by a Dunn test for post hoc testing using the 231 rstatix package (Kassambara, 2026). 232 To test hypotheses of the impacts on weekly growth rates across cultivars and treatment types, 233 we performed a linear mixed-effects model (LMM) fitted by a restricted maximum likelihood 234 (REML) method using the R packages lme4 (Bates et al., 2015) and lmerTest (Kuznetsova et 235 al., 2017). Week, treatment, cultivar, and treatment~cultivar were treated as fixed effects, and 236 growth as the response variable. Next, we performed a Type III ANOVA with Satterthwaite's 237 method with outputs from our LMM using the base R function aov(). Lastly, least-squares 238 means calculated from our LMM were used in downstream pairwise comparisons using the 239 emmeans R package (Lenth and Piaskowski 2026), adjusting for multiple comparisons using a 240 Tukey adjustment. Additionally, both biomass and growth rate data were visualized using 241 ggplot2 (Wickham 2016). 242 243 _Measurements of Physiological Responses to Drought_ [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 244 To test hypotheses of leaf physiological responses to drought, we measured leaf surface area 245 and circularity at the end of the 12-week growing season across all three treatment types. 246 Two leaf samples per individual (n=28) were collected from randomly selected plants and 247 photographed. Photographed images were scaled by a standardized reference and converted to 248 JPEGs. JPEG files were then uploaded into the software ImageJ (Schneider et al., 2012), and 249 each leaf was manually filled with a solid red overlay (RGB, 255, 0, 0) to create a region of 250 interest (ROI) image. Thereon, ImageJ was utilized to calculate a proxy for leaf surface area 251 using the number of pixels found within the ROI and leaf perimeter, using the border established 252 by the ROI. Lastly, circularity was calculated using the following equation by Hightower et al., 253 2024: (4 π ×Area)/Perimeter^2. Leaf surface areas and circularity were averaged across the two 254 leaves per individual per cultivar per treatment. Differences between cultivar or treatment types’ 255 leaf surface area and circularity values were then statistically tested using the base aov() 256 function in R, verified to meet standard ANOVA assumptions, and assessed for significance 257 using a Tukey Post Hot test with a p-value threshold of 0.05. 258 259 _Rhizospheric Collections and Sequencing_ 260 To explore the real-time effects of drought on hops throughout their lifecycle during the growing 261 season, we collected soil material at two time points: Week 2 and Week 12. We opted to collect 262 soil material at Week 2 to provide plants and their respective rhizospheric biomes the time to 263 acclimate to their new watering regime, and to explore both the immediate and long-term effects 264 of reduced watering regimes on the plants. Ten individuals per cultivar for each treatment type 265 were randomly selected for leaf and soil collection at both time points (n= 300). Soil material 266 adjacent to roots was collected two inches below the surface, scraping any present roots 267 available. Soil material was then stored on ice and placed in the -80C freezer for storage until 268 use. [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 269 Two individuals per cultivar per treatment were then randomly selected for downstream 270 sequencing (n=30). 0.5g of soil material collected from both Week 2 and Week 12 was loaded 271 into individual microcentrifuge tubes for DNA extraction, library prep, sequencing, and taxon 272 identification/quantification all performed by Zymo Research (Irvine, CA). For this study, we 273 opted to utilize both 16S and ITS sequencing to explore the entirety of the rhizospheric diversity 274 in microbial and fungal biomes. Briefly, gDNA was extracted and purified from collected soil 275 samples using the ZymoBIOMICS [®] -96 MagBead DNA Kit for 16S sequencing. Next, targeted 276 genes were amplified from the V3-V4 region of the 16S rRNA gene for 16S sequencing and the 277 ITS2 region for ITS sequencing using the _Quick_ -16S [™] NGS Library Prep Kit (Zymo Research, 278 Irvine, CA) and amplified via RT-PCR. Amplified fragments were then sequenced on Illumina [®] 279 Nextseq [™] with a P1 reagent kit for 600 cycles. 280 Raw reads generated from sequencing were then analyzed using the DADA2 pipeline (Callahan 281 et al., 2016) to remove potential sequencing errors and chimeric sequences and to identify 282 unique variants. Variants were then used to infer operational taxonomic units (OTUs) and OTU 283 compositions per individual using Uclust from QIIME v.1.9.1 (Caporaso et al., 2010). Identified 284 OTUs and percent abundance were then downloaded into a CSV file and used for 285 downstream statistical analysis. 286 287 _Patterns in Rhizospheric Diversity_ 288 To test hypotheses of the effects of treatment, duration of drought (start vs end), or cultivar 289 types on microbial abundance, we performed a linear mixed model test (LMM). Due to the 290 amount of microbial diversity discovered from 16S and ITS sequencing, we split our OTU 291 dataset into three groups: fungal (n=85), archaeal (n=6), and bacterial (n=174). We opted to 292 perform these LMMs separately rather than together to reduce model complexity and to reduce 293 computational complexity. For each microbial/fungal OTU data subset, an LMM was performed [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 294 using treatment, timepoint, and cultivar as the fixed effects and percent abundance within the 295 rhizosphere as the response variable and individual as the random effect. Bacterial, archaeal, 296 and fungal species identified as highly significant (p-value < 0.001) were then explored in the 297 literature to identify previously known drought tolerance associations. 298 299 _Analysis of Rhizospheric Nutrient Bioavailability_ 300 Soil was analyzed for extractable phosphate (PO4), nitrogen (nitrate and ammonium), and 301 dissolved organic carbon (C) to assess how P, N, and C were altered by hops varieties and 302 drought treatments. Extractable P was analyzed using the ascorbic acid method (Kuo 1996). 303 Briefly, approximately 5 g of fresh soil was added to 50 mL of a 0.05 _M_ HCl + 0.0125 _M_ H2SO4 304 extraction solution and shaken on a table shaker (E-Class, Barnstead/Lab-Line, Dubuque, IA) 305 for 10 minutes. Extracts were filtered using qualitative filter paper (P8, Fisher Scientific, Inc., 306 Pittsburg, PN), and the extract was measured at 880 nm after adding a molybdate color reagent 307 and an ascorbic acid reducing solution. Extractable N and dissolved organic C (DOC) were 308 measured from 0.5 _M_ K2SO4 extracts where approximately 10 g of fresh soil was extracted in 40 309 mL 0.5 _M_ K2SO4 after shaking on a table shaker for 60 minutes. For extractable N, filtered 310 extracts are analyzed for NH 4 (Mulvaney 1996) and NO 3 (Miranda et al. 2001) concentration by 311 absorbance at 667 nm (NH 4 ) and 540 nm (NO 3 ) using a spectrophotometer (Spectroquant 312 Pharo 300, Millipore Sigma, Burlington, MA, USA). For DOC, extracts were digested using the 313 Walkley-Black method (Sims and Haby 1971) and measured colorimetrically at an absorbance 314 of 600 nm with a spectrophotometer (Spectroquant Pharo 300, Millipore Sigma, Burlington, MA, 315 USA). Data are presented as concentrations per unit soil dry weight after correcting for the 316 moisture content of fresh soil. Dissolved inorganic N is defined as the sum of extractable NH4 + 317 NO3. 318 319 **Results:** [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 320 321 _Impact on Biomass Production_ 322 To assess whether reduced watering strategies impacted hops aboveground biomass 323 production over the 12-week experimental period, we performed either a two-way ANOVA test 324 (Cascade, Centennial, Chinook and Comet) or a non-parametric Kruskal-Wallis test (CTZ) on 325 collected biomass values (g). Cascade (df= 2, F= 0.064, p= 0.939), Centennial (df= 2, F= 0.918, 326 p= 0.403), and Comet (df= 2, F=2.899, p= 0.067) showed no significant difference in biomass 327 production between control, moderate, and severe treatment types (Figure 1; Table 2). 328 Chinook’s (df=2, F= 21.020, p= 7.98E-07) average biomass was the highest under moderate 329 treatment type and lowest under the control treatment (Table 1; Figure 1). Biomass production 330 significantly differed between all treatment types, with our Tukey post-hoc test revealing 331 moderate versus control treatments displaying the greatest impact (Table 3). 332 A non-parametric Kruskal-Wallis test on CTZ’s biomass data (due to failed parametric ANOVA 333 assumptions) revealed that biomass production significantly differed between treatment types 334 (df=2, x [2] = 23.611, p= 7.46E-06) (Table 2). A post hoc Dunn test revealed significant differences 335 between control versus severe (p= 9.95E-04) and moderate versus severe (p= 9.27E-06) 336 models (Table 3), with no significant difference in biomass production between control and 337 moderate (p= 0.164) regimes. 338 When treatment type was used as a fixed effect, aboveground biomass production varied 339 significantly for all three treatment types; control (df= 4, F= 9.587, p= 1.63E-06), moderate 340 drought (df= 4, F= 10.22, p=8.87E-07) and severe drought (df= 4, F= 5.388, p=5.58E-04). Tukey 341 post-hoc testing revealed that under the control treatment, Comet and CTZ produced 342 significantly larger (p < 0.05) aboveground biomass than Cascade (~Comet, p=5.67E-05; ~CTZ, 343 p= 3.84E-03), Centennial (~Comet, p= 6.84E-04; ~CTZ, p= 0.048), and Chinook (~Comet, 344 p=1.32E-04; ~CTZ, p= 6.17E-03) (Figure 2; Table 4). Under the moderate drought treatment, 345 Chinook displayed insignificant differences in aboveground biomass when compared to Comet [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 346 (p= 0.906) and CTZ (p= 0.751). Under the severe treatment type, aboveground biomass most 347 noticeably differed between Comet and two other cultivars, with Comet displaying significant 348 impacts on biomass production compared to Cascade (p= 4.19E-03) and CTZ (p= 0.021), but 349 insignificant differences with Centennial (p= 0.537) and Chinook (p= 0.979) (Figure 2; Table 4). 350 351 _Linear Mixed Model and Weekly Growth Rates_ 352 ANOVA and Tukey post hoc tests revealed that treatment type (df= 2, F= 10.389, p= 3.18E-05), 353 cultivar (df=4, F= 10.5697, p= 1.66E-08), and week (df= 10, F= 67.417, p <2.2E-16) 354 significantly impacted weekly growth rates over the 12-week growing period (Table 5). Post-hoc 355 testing exploring weeks as the fixed effect revealed that Weeks 2-4 (mid to late July) 356 experienced significantly higher weekly growth rates compared to all other weeks during the 12 357 week growing period (Table 6). Post-hoc testing using treatment type as the fixed effect 358 revealed that weekly growth rates did not significantly differ between the control and moderate 359 treatment types (z= 0.363, p= 0.9298); however, significant differences were identified between 360 control versus severe (z= 4.053, p= 0.0001) and moderate versus severe treatment types (z= 361 3.770, p= 0.0005). Tukey post-hoc tests exploring cultivar type as the fixed effect to evaluate 362 which cultivars differed significantly in weekly growth rates revealed that while no cultivar types 363 displayed significant differences in weekly growth rates compared to all other cultivars, CTZ was 364 found to display significant differences with three out of four cultivars (Cascade, Chinook and 365 Comet; Table 7). 366 Further post-hoc testing accounting for a treatment~cultivar interaction revealed that under the 367 control treatment, weekly growth rate did not significantly differ between any of the cultivar types 368 (Table 8). However, under the moderate drought treatment type, Comet and CTZ displayed 369 significant differences in weekly growth rates compared to Cascade (~Comet, p= 0.0305; ~CTZ, 370 p= 0.0051) and Chinook (~Comet, p= 0.0023; ~CTZ, p= 0.0004), with CTZ and Comet (z= [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 371 5.165, p <0.0001) eventually displaying significant differences in weekly growth rates under the 372 severe drought regime (Table 8). 373 374 _Leaf Surface Area and Leaf Circularity_ 375 Treatment (df= 2, F= 6.038, p= 0.01398), cultivar (df= 4, F= 35.638, p=6.85E-07), and 376 treatment~cultivar (df= 8, F= 5.733, p= 0.00292) interaction significantly impacted leaf surface 377 area (Table 9). Post hoc testing examining treatment type as a fixed effect revealed that leaf 378 surface area significantly differed between the moderate versus severe treatment types (p= 379 0.01297) but showed insignificant differences between the control and moderate treatment (p= 380 0.06977) as well as the control and severe treatment (p= 0.67813) types (Table 10), with the 381 severe treatment type displaying the greatest variability in leaf surface area (Figure 3). 382 When cultivar type was assessed as the fixed effect, our Tukey post-hoc test revealed that CTZ 383 and Comet were significantly larger in leaf surface area than Cascade (~CTZ, p= 0.000052; 384 Comet, p= 0.000001), Centennial (~CTZ, p= 0.000258; ~Comet, p= 0.000006), and Chinook 385 (~CTZ, p= 0.011335; ~Comet, p= 0.094796) (Figure 3b; Table 10). Additionally, Cascade was 386 shown to display the least leaf surface area variability, while Comet displayed both the largest 387 leaf surface area as well as the greatest variability (Figure 3b). Post-hoc testing focusing on 388 treatment~cultivar interaction showed that Cascade, Centennial, and Chinook most significantly 389 differed in leaf surface area from Comet (~Cascade, p= 0.000009; ~Centennial, p= 0.000049; 390 ~Chinook= 0.000056) and CTZ (~Cascade, p= 0.001782; ~Centennial, p= 0.015704; ~Chinook= 391 0.018768) under the severe drought treatment, with p-values supporting no significant 392 difference in leaf surface area under the control or moderate drought treatments. 393 ANOVA test of leaf circularity revealed that while treatment type (df= 2, F= 2.053, p= 0.16797) 394 and a treatment~cultivar (df= 8, F= 1.552, p= 0.23094) did not significantly impact leaf 395 circularity, cultivar type (df= 4, F= 2.053, p= 2.47E-03) was found to have a significant impact on 396 leaf circularity (Figure 4; Table 11). Post hoc testing of cultivar type revealed that Comet [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 397 displayed significantly lower leaf circularity compared to Cascade (p= 0.010855), Centennial (p= 398 0.004089), and Chinook (p= 0.009506) and was insignificantly lower when compared to CTZ 399 (p= 0.273186) (Figure 4b; Table 12). 400 401 _Soil Microbial and Fungal Community Dynamics in Response to Drought_ 402 From ITS sequencing, we were able to recover 97 fungal OTUs found across our 28 sampled 403 hops rhizospheres. ANOVA testing using percent abundance data generated from ITS 404 sequencing found that neither cultivar (df= 4, F= 0.0079, p= 0.9999), treatment (df= 2, F= 405 0.0038, p= 0.9962), nor timepoint (df= 1, F= 0.0165, p= 0.8979) impacted fungal composition 406 within the rhizosphere (Table 13). However, accounting for an interaction between percent 407 abundance for OTU and each of our fixed effects, ANOVA testing revealed that OTU percent 408 abundance did identified did significantly differ between cultivar type (df= 384, F= 2.418, p< 2E 409 16) and timepoint (df= 384, F= 2.7637, p= 1.03E-11) but not treatment type (df= 384, F= 410 0.6369, p= 1.0000) (Table 13). Post hoc testing accounting for a cultivar~OTU_identified 411 interaction revealed that three fungal families, Aspergillaceae (t= 6.575, p= 5.54E-11), 412 Cephalothecaceae (t= 4.154, p= 3.33E-05), and Cladosporiaceae (t= 16.366, p< 2E-16) 413 displayed significant differences in percent abundance between cultivar types. 414 Aspergillaceae (t= 18.303, p< 2E-16), Cephalothecaceae (t= 7.572, p= 4.50E-14), and 415 Cladosporiaceae (t= 21.069, p< 2E-16) were identified as statistically impacted in percent 416 abundance between time points by our LMM, as well as the addition of Chaetomiaceae (t= 417 3.219, p= 1.30E-03) and Microascaceae (t= 3.597, p= 3.26E-04). 418 Using targeted 16S sequencing, we recovered 6 archaeal OTU’s across all sampled 419 rhizospheres. Neither cultivar (df= 4, F= 1.9382, p= 0.1039826), treatment type (df= 2, F= 420 0.2358, p= 0.7900), nor time point (df= 1, F= 2.0341, p= 0.154767) independently impacted 421 archaeal percent abundance, however, did affect the composition of specific archaeal families 422 (Table 13). Post hoc testing revealed that of the six identified archaeal OTUs, only one clade, [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 423 Thaumarchaeota, was found to significantly differ in percent abundance across cultivar types 424 and between timepoints. Using timepoint as our fixed factor, Thaumarchaeota percent 425 abundance was significantly higher (t= 3.269, p= 0.0012) at the start of the experiment than at 426 the end of the twelve weeks (Fig. 5). Additionally, post-hoc testing using cultivar as the fixed 427 effect unveiled that two cultivars, Chinook (t= 3.005, p= 0.00288) and Comet (t= 4.402, p= 428 1.48E-05), displayed significantly higher percent abundance of Thaumarchaeota compared to 429 the other three tested cultivar types (Fig 5). 430 16S sequencing targeting bacteria recovered 175 bacterial OTUs across our sampled hops 431 rhizospheres. ANOVA tests using cultivar (df= 4, F= 0.2684, p= 0.8984), treatment type (df= 2, 432 F= 0.1436, p= 0.8662), and timepoint (df= 2, F= 0.1639, p= 0.6856) revealed that, 433 independently, these three fixed effects did not significantly impact total bacterial OTU percent 434 abundance; however, each effect was found to significantly impact particular OTUs’ percent 435 abundance independently (Table 15). Using timepoint as a fixed factor, over half (64.5%; 436 n=111) of the identified bacterial families showed a significant (p-value < 0.05) difference in their 437 total percent abundance, with nine bacterial families displaying highly significant (p-value < 438 0.001) changes in percent abundance during the 12-week experimental period. 439 Using cultivar as a fixed effect, seven bacterial OTU’s significantly differed in percent 440 abundance between cultivar types; Bacillaceae (t= 37.873, p< 2E-16), Nocardioidaceae (t= 441 4.847, p=1.28E-06), Paenibacillaceae (t= 21.181, p< 2E-16), Planctomycetaceae (t= 2.008, p= 442 4.47E-02), Rhodothermaceae (t= -2.101, p= 3.57E-02), Streptomycetaceae (t= 4.967, p= 6.92E 443 07) and Streptosporangiaceae (t= 8.633, p< 2E-16). Of the seven bacterial OTU’s identified as 444 varying significantly between cultivar types, only three OTU’s, Bacillaceae, Paenibacillaceae, 445 and Streptosporangiaceae, showed significant differences in percent abundance for each 446 cultivar type, whereas other bacterial OTUs such as Planctomycetaceae displayed significant 447 differences in percent abundance for only one particular cultivar, possibly displaying novel 448 associations (Table 16; Figure 6). [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 449 Using treatment as the fixed effect, eight bacterial OTU’s significantly differed in percent 450 abundance between the start and end of the experiment (Table 17), several of which have been 451 detected between other fixed factors as well, as described above. When the LMM was 452 examined for treatment~OTU interaction as the fixed effect, only one bacterial OTU significantly 453 differed in percent abundance, Bacillaceae (moderate treatment, t= 3.191, p= 0.00142; severe 454 treatment, t= -4.342, p= 1.42E-05). Visualization of percent abundance between treatment types 455 revealed that for Bacillaceae, percent abundance was highest in the moderate drought regime 456 and lowest in the severe regime, with the control treatment having a significantly lower percent 457 abundance compared to the moderate treatment (Figure 7). 458 459 _Impact on Rhizospheric Nutrient Bioavailability_ 460 ANOVA testing of five major bioavailable nutrients (PO4, NH4, NO3, DIN and DOC) revealed 461 cultivar type significantly impacted nutrient bioavailability for four of the five nutrient types. PO4 462 (df= 4, F= 3.159, p= 0.045), NO3 (df= 4. F= 14.211, p< 0.001), DIN (df= 4, F= 7.136, p= 0.002) 463 and DOC (df= 4, F= 5.576, p= 0.006) bioavailability were found to be significantly impacted by 464 cultivar type. Under both control conditions and the moderate drought regime, CTZ displayed 465 the greatest amount of extractable PO4 compared to other cultivar types (Figure 8a). However, 466 under severe drought conditions, Comet not only displayed the greatest amount of bioavailable 467 PO4 compared to other cultivar types, but displayed the highest overall amount when compared 468 to moderate drought and control treatments. 469 NO3 bioavailability was strongly impacted by both cultivar type (df= 4, F= 14.211, p<0.001) and 470 treatment type (df=2, F= 5.691, p= 0.014) independently and insignificantly when tested for a 471 cultivar~treatment interaction (df=8, F= 1.548, p= 0.222). 472 NO3 bioavailability was highest under the moderate drought regime for Chinook and under the 473 severe treatment for Comet and CTZ (Figure 8b). As for Cascade and Centennial, NO3 474 bioavailability remained relatively consistent between treatment types. Extractable DIN was [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 475 found to be greatest in the rhizosphere of Chinook cultivars under all three watering regimes, 476 with moderate regime displaying over a 2-fold increase in extractable DIN when compared to 477 the other tested cultivars. Additionally, Comet and CTZ observed the greatest extractable DIN 478 under severe drought conditions (Figure 8c). 479 Lastly, ANOVA testing of extractable DOC values revealed that cultivar (df= 4, F= 5.576, p= 480 0.006) and treatment type (df= 2, F= 7.306, p= 0.006) strongly impacted bioavailability of DOC 481 within the rhizosphere. Additionally, an interaction between cultivar type and treatment type was 482 found to strongly impact extractable DOC values (df= 8, F= 7.362, p< 0.001). Cascade, Chinook 483 and CTZ displayed the highest extractable DOC values under the control treatment as well as 484 overall between treatments types (Figure 8d). Under moderate drought conditions, Comet was 485 found to display the greatest extractable DOC values as well as displaying the highest between 486 the three treatment types. Lastly, Centennial was the only cultivar type to display the greatest 487 amount of extractable DOC under severe drought like conditions, nearly doubling values 488 detected for moderate and control conditions. 489 490 **Discussion** 491 _American Cultivars Displayed Differential Tolerance to Reduced Watering Strategies_ 492 The primary goal of our study was to explore hops' physiological and rhizospheric responses to 493 reduced watering regimes. As expected, our study revealed that as irrigation levels decreased 494 (moving from control to severe treatment group), phenotypic characteristics such as biomass, 495 weekly growth rate, and leaf shape/size were significantly impacted as a response to the 496 reduced watering regimes. An agricultural product during hops production is the mature hops 497 flower known as the ‘cone’. In order for hops flowers to be produced, plants must complete their 498 vegetative growth phase, achieving a targeted height between 5- 6 m in order for ideal yield 499 conditions to occur (Acosta-Ragnel et al., 2021; Rossini et al., 2021). While our study showed 500 that all tested cultivars did become significantly impacted by reduced watering regimes, cultivars [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 501 such as Chinook, CTZ, and Comet were found to produce greater biomass and greater weekly 502 growth under drought conditions than cultivars like Cascade and Centennial. 503 Across treatment types, only Comet was found to display no significant differences in biomass 504 production between treatment types, whereas cultivars such as CTZ and Chinook did display 505 some significant differences between treatment types, but overall produced more aboveground 506 biomass than that of Cascade and Centennial. Our ANOVA of weekly growth rates revealed that 507 under the severe drought regime, Comet grew significantly more biomass than Centennial and 508 CTZ, insignificantly more than Cascade (p-value=0.0867) and very similar to that of Chinook. 509 Our ANOVA’s of leaf size and circularity yielded similar patterns as found above. For instance, 510 under ideal watering conditions, Comet and CTZ displayed a significantly larger leaf surface 511 area than that of Cascade but were not significantly larger than Centennial and Chinook. 512 However, under the severe treatment, Comet displayed a significantly larger leaf size than 513 Cascade, Centennial, and Chinook, with CTZ displaying a reduction in leaf size, insignificantly 514 compared to Comet, highlighting the impact of reduced watering strategies. Under the severe 515 drought regime, Comet not only displayed the largest surface area amongst the other four 516 cultivars; additionally, this cultivar saw an increase in surface area compared to that of the 517 control and moderate treatment. Generally, however, leaf sizes began to reduce as water 518 availability decreased, a trend expected for plants under drought stress in order to reduce the 519 plant's total surface area for water loss via transpiration from photosynthesis (Mohanta et al., 520 2023). 521 Similar to leaf size, as drought conditions continue to worsen, it is expected that leaves will 522 become narrower, therefore less circular, as a mechanism to further reduce their surface area to 523 mitigate water loss (Varsamis et al., 2022). Under the control treatment, Comet displayed the 524 lowest leaf circularity score; however, this difference was nonsignificant compared to the 525 cultivars. As water stress increased and water availability decreased, Cascade, Centennial, 526 Chinook, and CTZ generally increased their leaf circularity while Comet remained constant, [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 527 leading to significantly lower leaf circularity scores between Comet and all other cultivars under 528 the severe drought regime. 529 530 _Belowground Associations As Possible Mechanisms for Drought Resiliency_ 531 Genetic variation associated with drought response traits may vary phenotypically between 532 cultivars; however, this is not the only possible mechanism for conferring drought tolerance. 533 Belowground associations with rhizospheric microbes may help foster resiliency to drought 534 through mutualistic relationships between crops and their neighboring microbial communities. 535 16S sequencing of soil samples collected from the rhizospheric region of 30 individuals across 536 our five cultivars and three treatment types revealed that overall, hops rhizospheric communities 537 are dominated by Bacillus, making up 10-30% of the overall bacterial diversity in the region. 538 _Bacillus_ composition was found to be on average highest in CTZ and differed significantly 539 between Cascade and Comet. Interestingly, Comet was found to have the lowest percent 540 abundance of this microbe, a trend unexpected due to its unwavering phenotypic resistance in 541 the face of drought. 542 ANOVA analysis using timepoint (start vs end) as a fixed effect found that 64.5% of the 543 identified bacterial families displayed a significant difference in their percent abundance from the 544 start vs the end of the 12-week experimental period, with nine families displaying highly 545 significant differences (p-value < 0.001). Of these nine families, four families (Bacillaceae, 546 Paenibacillaceae, Planctomycetaceae and Staphylococcaceae) were found to significantly vary 547 in percent abundance between cultivar types. Interestingly, three of these four families are 548 known to contain species of bacteria that have been previously classified as PGPRs, with 549 members of the fourth family, Planctomycetaceae, being shown to improve soil structure, 550 nutrient availability, and nitrogen cycling under periods of osmotic stress, such as drought 551 (Tugbaeva et al., 2025). [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 552 Patterns of association between microbial families and cultivar types were also observed. For 553 instance, Chinook and Comet, two cultivars found to display similar phenotypic responses to 554 drought, were also found to be the only two associated with Staphylococcaceae and display 555 similar patterns in abundance with Paenibacillaceae and Planctomycetaceae. Additionally, 16s 556 sequencing of the archaea found within the rhizosphere highlighted that Chinook and Comet 557 displayed the highest abundance of the archaeal family Thaumarchaeota, the only archaea 558 found to be significantly impacted by reduced watering regimes. Abundance of Thaumarchaeota 559 was significantly impacted by watering regimes in Chinook and Comet compared to the 560 remaining cultivars, with abundance highest at the start of the experiment and significantly 561 declining by the end of the 12 weeks. 562 Interestingly, Thaumarchaeota has been previously reported to confer potential drought 563 tolerance under arid conditions by converting ammonium in the soil to nitrate via nitrification, a 564 substance easily absorbed by the roots and used directly for the proper growth and 565 development of the plant (Burns et al., 2025; Kristy et al., 2022). Data from our experiment 566 revealed that while extractable ammonium was found to not be significantly impacted by either 567 cultivar or treatment type, extractable nitrate varied very significantly between cultivar and 568 treatment types. For Chinook, extractable nitrate was found to be greatest overall under 569 moderate drought regime. Under severe drought conditions however, Chinook still displayed the 570 greatest amount of extractable nitrate compared to other cultivars while Comet displayed an 571 intracultivar best. 572 Given that Chinook and Comet were two of our cultivars that were found to display the least 573 impact on their growth and development by reduced watering strategies, we hypothesize that 574 microbes presented here, such as Thaumarchaeota, should be further explored for their 575 potential as possible drought conferring mechanisms within the hops rhizosphere. If future 576 studies yield similar findings, potential inoculation treatments may be developed to help aid in [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 577 conferring drought tolerance, a strategy already being applied to other major cash crops such as 578 maize (Curá et al., 2017; Francioli et al., 2025). 579 580 _The Future of Hops Breeding_ 581 Production of hops and quality of product are expected to decline over the next 50 years as a 582 response to warming temperatures via climate change (Mozny et al. 2023). In order for breeders 583 to best mitigate these effects, they must first understand the potential response key cultivars 584 may have under these future conditions. Here, we opted to use five of the most cultivated hop 585 cultivars in the United States to assess their potential to withstand reduced watering regimes. 586 Phenotypic data generated from our study suggest that of the five cultivated varieties tested, 587 Comet may serve as the best individual to focus selective breeding tactics towards. We found 588 that under severe drought conditions (50% of the recommended watering), biomass and weekly 589 growth rates did not significantly differ between individuals in the control treatment and the 590 severe treatment, indicating potential resistance to osmotic stress. Additionally, leaf 591 morphological traits such as leaf shape indicated that Comet possesses additional drought 592 adaptive traits such as narrow leaf shape, a tactic used to minimize leaf surface area and 593 reduce photosynthetic productivity in a time of drought stress. 594 This begs the question: is Comet the future of hops breeding? The short answer: no. The long 595 answer: It is a potential source of some adaptive variation, but our study revealed an even 596 deeper potential pattern with broader implications. While ANOVAs were performed for each of 597 our tested morphological traits, highlighting the significant differences in Comet's growth 598 compared to the other four cultivars, a pattern of “grouping” or “response clusters” occurred in 599 the data as well. Throughout our study, we found that Cascade and Centennial displayed similar 600 responses to drought (such as lower biomass, slower growth rates, etc.) to each other than that 601 of Chinook, Comet, and CTZ. Furthermore, in several analyses, Chinook was found to perform 602 very similarly to Comet or, in some cases, better (such as biomass growth under moderate [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 603 drought conditions) in the face of moderate and severe drought. Additionally, profiling of the 604 rhizospheric communities across our 30 tested cultivars found that Comet and Chinook 605 displayed similar percent abundance for several key PGPR, including being the only two 606 cultivars to foster bacteria from the Staphylococcaceae. 607 Is this coincidence or something deeper? When we take a step back and look at the pedigree of 608 these two cultivars, we find a commonality: an extensive breeding history of backcrossing with 609 ‘wild American’ breeding lines. In both these cases, their breeding history indicates being 610 backcrossed with wild Utah American hops (USDA Utah 524 and 526). Of the three wild hop 611 varieties known to occur throughout North America, only one species is found in the 612 southwestern United States: _var. neomexicanus_ . Coincidentally, this region also typically 613 displays warmer temperatures and lower precipitation patterns than those of its Pacific 614 Northwest counterparts; however, in 50 years from now, climate in this region may be similar to 615 the conditions felt in the future throughout the PNW. 616 617 _Conclusion_ 618 The primary goal of our study was to evaluate economically important hops and their phenotypic 619 and rhizospheric responses to reduced watering strategies, a very real and near-future 620 possibility. We found that out of the five cultivars we tested, Comet and Chinook consistently 621 performed best under reduced watering strategies, serving as excellent candidates for future 622 breeding efforts. Metabarcoding analysis revealed that hop plants generally harbor several plant 623 growth-promoting rhizobacteria; however, the composition of the rhizosphere significantly 624 differed between tested cultivars, with Comet and Chinook displaying several similarities at the 625 rhizospheric level. Given our findings, we highly suggest that future breeding efforts specifically 626 focus on introgression with wild native American hops populations, specifically of _var._ 627 _neomexicanus_, in order to remain a competitive player in future hops production. 628 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 629 **Acknowledgments** 630 631 We would like to thank members of the Sethuraman Lab for their valuable comments on earlier 632 versions of this manuscript. We extend our sincere thanks to Dr. Lluvia Flores-Renteria for 633 permitting use of the SDSU Alvarado Greenhouse. We would also like to thank the Fall 2024 634 and 2025 Class of BIOL 596- Research Methods in Agricultural Sciences at San Diego State 635 University for their help with data collection for a portion of the growing season. 636 637 **Authors Contributions:** 638 AMA designed, implemented, collected, analyzed all data, and wrote the manuscript. GV 639 performed all the soil analyses. AS and GV acquired funding and co-wrote the manuscript. CM, 640 KK, ZG helped with all the sample collection, processing, DNA extractions. MG, AA helped with 641 computing the leaf surface area measures. All authors reviewed and approved the final draft. 642 **Conflict of Interest:** 643 644 There are no conflicts of interest to declare. 645 646 **Funding Statement:** 647 648 This work was funded by USDA-HSI:2022-77040-38529 to PI Sethuraman and co-PIs Vourlitis 649 and Jancovich, and DE-SC0025673 to PI Anand (SFSU) and co-PI Sethuraman. 650 651 652 653 654 655 656 657 658 659 660 661 662 663 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 664 **Figures:** 665 666 **a)** 667 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 668 **b)** 669 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 670 **c)** 671 672 673 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 674 **d)** 675 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 676 **e)** 677 678 679 680 **Fig 1.** Boxplots of measured aboveground biomass (g) for Cascade (a), Centennial (b), Chinoo **k** 681 (c), Comet (d) and CTZ (e) across the three treatment types control (red), moderate (yellow) 682 and severe (blue). 683 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 684 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 685 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 686 687 **Fig 2.** Boxplots of aboveground biomass production in grams, across cultivars within watering 688 regimes - (a) Control, (b) moderate drought, and (c) severe drought Teal= Cascade, orange= 689 Centennial, purple= Chinook, pink= Comet and green= CTZ. 690 691 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 692 a) 693 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 694 b) 695 696 **Figure 3.** Boxplots of measured leaf surface area for each treatment type (a) or cultivar type (b) **.** [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 697 a) 698 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 699 b) 700 701 702 **Figure 4.** Box plots of leaf circularity by treatment type (a) or cultivar type (b). Leaf circularity 703 values were calculated using the following formula: (4 π ×Area)/Perimeter^2, as suggested by 704 Hightower et al. 2024. 705 706 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 707 708 **Figure 5.** Boxplot representing the average percent abundance of the archaeal phylum 709 Thaumarchaeota across the five test cultivar types (Cascade, Centennial, Chinook, Comet, and 710 CTZ) between two time points, the start and end of the 12-week drought experimental period. 711 712 a) 713 714 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 715 b) 716 717 718 c) 719 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 720 d) 721 722 **Figure 6.** Violin plots of hops cultivars’ percent abundance across the five cultivar types for the 723 following bacterial families: a) Bacillaceae, b) Paenibacillaceae, c) Planctomycetaceae, d) 724 Streptosporangiacae 725 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 726 727 **Figure 7** . Boxplot representing the bacterial family Bacillaceae measured percent abundance 728 for each of the following treatment types: control, moderate, and severe. 729 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 730 a) 731 732 733 b) 734 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 735 736 c) 737 738 739 d) 740 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 741 **Figure 8.** Measured values for extractable nutrient values for phosphate (a), nitrate (b), 742 dissolved inorganic nitrogen (c) and dissolved organic carbon (d) for each cultivar type: 743 Cascade (dark blue), Centennial (orange), Chinook (green), Comet (light blue) and CTZ 744 (purple). 745 746 747 **Tables** 748 |Cultivar|Treatment<br>Type|Sample<br>Size|Average<br>Biomass(g)|STDEV(+/ -)|AverageWeekly<br>GrowthRate(in)|STDEV(+/ -)| |---|---|---|---|---|---|---| |**Cascade**|Control|20|13.070|1.761|1.440|4.053| |**Cascade**|Moderate|17|13.318|2.249|0.956|3.988| |**Cascade**|Severe|19|13.212|2.361|0.958|5.239| |**Centennial**|Control|29|13.854|2.241|1.574|4.818| |**Centennial**|Moderate|26|14.545|2.126|1.716|4.587| |**Centennial**|Severe|28|14.375|1.514|1.591|4.637| |**Chinook**|Control|9|12.544|1.307|0.942|4.701| |**Chinook**|Moderate|9|17.386|2.517|0.483|3.954| |**Chinook**|Severe|22|14.870|1.303|-0.254|4.625| |**Comet**|Control|12|17.394|2.737|1.641|4.680| |**Comet**|Moderate|15|16.631|1.835|2.012|4.518| |**Comet**|Severe|15|15.345|2.202|1.403|4.498| |**CTZ**|Control|26|15.271|3.828|1.774|4.593| |**CTZ**|Moderate|21|16.405|1.698|2.049|4.980| |**CTZ**|Severe|24|13.137|3.005|1.897|5.320| 749 750 **Table 1.** Table of sample size, average biomass, and weekly growth rates for each cultivar type 751 across their various treatments. 752 753 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 754 |Cascade|2000<br>.|0580<br>.|0288<br>.|0064<br>.|0939<br>.|N/A| |---|---|---|---|---|---|---| |**Centennial**|2.000|7.240|3.621|0.918|0.403|N/A| |**Chinook**|2.000|0.466|0.233|21.020|7.98E-07|ArcSinh| |**Comet**|2.000|29.360|14.680|2.899|0.067|N/A| |**CTZ***|2.000|chi-squared=23.611|chi-squared=23.611|chi-squared=23.611|7.46E-06|YeoJohnson| |**Control**|4.000|28.080|7.021|9.587|1.63E-06|orderNorm| |**Moderate**|4.000|172.200|43.040|10.220|8.87E-07|N/A| |**Severe**|4.000|18.470|4.617|5.388|5.58E-04|orderNorm| 755 **Table 2.** Summary statistics and p-values generated from ANOVA test for aboveground 756 biomass values (g). Fixed effects tested: cultivar type (Cascade, Centennial, Chinook, Comet, 757 CTZ) and treatment type (Control, Moderate, and Severe) with aboveground biomass as the 758 response variable. * indicates the use of a Kruskal-Wallis test in lieu of an ANOVA test when 759 necessary. 760 761 762 |Col1|Moderate~Control|Severe~Control|Severe~Moderate| |---|---|---|---| |**Cascade**|0.934|0.976|0.988| |**Centennial**|0.406|0.585|0.947| |**Chinook**|4.00E-07|6.17E-04|2.52E-03| |**Comet**|0.658|0.060|0.273| |**CTZ**|0.164|9.95E-04|9.27E-06| 763 **Table 3.** Generated p-values from post-hoc (Tukey or Dunn) testing between treatment types 764 for each cultivar type using biomass as the response variable. 765 766 |Col1|Cascade|Centennial|Chinook|Comet|Col6| |---|---|---|---|---|---| |**Centennial**|0.7850847|-|-|-|**Control**<br>**Treatment**| |**Chinook**|0.9619241|0.5170894|-|-|-| |**Comet**|5.67E-05|6.84E-04|1.32E-04|-|-| |**CTZ**|3.84E-03|0.0480085|6.17E-03|0.3134526|0.3134526| |**Centennial**|0.3165009|-|-|-|**Moderate**| [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) |Chinook|645E 05<br>. -|510E 03<br>. -|-|-|Drought| |---|---|---|---|---|---| |**Comet**|1.68E-04|0.0195595|0.9061582|-|-| |**CTZ**|1.37E-04|0.0222116|0.7518333|0.9975417|0.9975417| |**Centennial**|0.090202|-|-|-|**Severe**<br>**Drought**| |**Chinook**|8.93E-03|0.8233731|-|-|-| |**Comet**|4.19E-03|0.5369137|0.9794279|-|-| |**CTZ**|0.9432712|0.3341307|0.0468495|0.0212125|0.0212125| 767 768 **Table 4.** p-values generated from Tukey Post Hoc testing of ANOVA results using treatment 769 type as the fixed effect and aboveground biomass as the response variable. 770 771 772 |FixedEffect|SumSq|MeanSq|NumDF|DenDF|F -value|Pr(>F)| |---|---|---|---|---|---|---| |**Cultivar**|681.700|170.410|4|3,190.000|10.570|1.66E-08| |**Week**|10,869.600|1,086.960|10|3,190.000|67.417|<2.2E-16| |**Treatment**|335.000|167.510|2|3,190.000|10.389|3.18E-05| |**Cultivar:Week**|3,323.300|83.080|40|3,190.000|5.153|<2.2E-16| |**Cultivar:Treatment**|387.400|48.420|8|3,190.000|3.003|2.33E-03| |**Week:Treatment**|1,773.600|88.680|20|3,190.000|5.500|3.75E-14| |**Cultivar:Week:Treatment**|2,801.000|35.010|80|3,190.000|2.172|1.37E-08| 773 **Table 5.** Summary statistics generated from a Type III ANOVA test using Satterthwaite's 774 method on outputs of the linear mixed model (LMM). Cultivar, treatment, and week served as 775 fixed effects in the LMM, and weekly growth rates were response variables. 776 777 |Col1|Week2|Week3|Week4|Week5|Week6|Week7|Week9|Week10|Week<br>11|Week12| |---|---|---|---|---|---|---|---|---|---|---| |**Week 1**|<0.0001|<0.0001|0.0977|0.98|0.0357|0.0137|<0.0001|0.9934|0.0241|0.9733| [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 778 |Week2|-|<00001<br>.|<00001<br>.|<00001<br>.|<00001<br>.|<00001<br>.|<00001<br>.|<00001<br>.|<00001<br>.|<00001<br>.| |---|---|---|---|---|---|---|---|---|---|---| |**Week 3**|-|-|<0.0001|<0.0001|<0.0001|<0.0001|<0.0001|<0.0001|<0.0001|<0.0001| |**Week 4**|-|-|-|0.0013|<0.0001|<0.0001|<0.0001|0.0026|<0.0001|0.001| |**Week 5**|-|-|-|-|0.5751|0.3756|<0.0001|1.000|0.4884|1.000| |**Week 6**|-|-|-|-|-|1.000|0.207|0.4562|1.000|0.6111| |**Week 7**|-|-|-|-|-|-|0.3657|0.275|1.000|0.4089| |**Week 9**|-|-|-|-|-|-|-|<0.0001|0.2677|0.0001| |**Week 10**|-|-|-|-|-|-|-|-|0.3744|1.000| |**Week 11**|-|-|-|-|-|-|-|-|-|0.5242| 779 **Table 6.** Matrix of p-values generated from Tukey’s post-hoc test using average growth rate 780 values computed for each week over the 12-week growing period. 781 782 783 |Col1|Centennial|Chinook|Comet|CTZ| |---|---|---|---|---| |**Cascade**|0.0625|0.1109|0.9931|0.0004| |**Centennial**|-|<0.0001|0.3073|0.4852| |**Chinook**|-|-|0.0662|<0.0001| |**Comet**|-|-|-|0.011| 784 **Table 7** . p-values generated from Tukey post hoc comparisons of the five tested cultivar types 785 and their weekly growth rates over the 12-week growing period. 786 787 |Col1|Cascade|Centennial|Chinook|Comet|Col6| |---|---|---|---|---|---| |**Centennial**|0.9798|-|-|-|**Control**<br>**Treatment**| |**Chinook**|0.8933|0.6432|-|-|-| |**Comet**|0.9969|1|0.8031|-|-| |**CTZ**|0.8335|0.9837|0.4146|0.9854|0.9854| |**Centennial**|0.112|-|-|-|**Moderate**<br>**Drought**| |**Chinook**|0.6887|0.0089|-|-|-| |**Comet**|0.0305|0.9069|0.0023|-|-| [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) |CTZ<br>Centennial<br>Chinook<br>Comet<br>CTZ|00051<br>.|07265<br>.|00004<br>.|09995<br>.|Col6| |---|---|---|---|---|---| |**CTZ**<br>**Centennial**<br>**Chinook**<br>**Comet**<br>**CTZ**|0.5047|-|-|-|**Severe**<br>**Drought**| |**CTZ**<br>**Centennial**<br>**Chinook**<br>**Comet**<br>**CTZ**|0.2729|0.0071|-|-|-| |**CTZ**<br>**Centennial**<br>**Chinook**<br>**Comet**<br>**CTZ**|0.0867|0.0003|0.9997|-|-| |**CTZ**<br>**Centennial**<br>**Chinook**<br>**Comet**<br>**CTZ**|0.0779|0.8428|0.0003|<0.0001|<0.0001| 788 789 **Table 8** . p-values generated from Tukey post-hoc comparisons evaluating a treatment~cultivar 790 interaction using weekly growth rates as the response variable for each of the five cultivar types 791 (Cascade, Centennial, Chinook, Comet and CTZ) under each of the three drought treatment 792 types (control, moderate and severe). 793 794 795 |Col1|Df|Sum Sq|Mean Sq|F -value|Pr(>F)| |---|---|---|---|---|---| |**Treatment**|2|121.9|60.9|6.038|1.40E-02| |**Cultivar**|4|1438.7|359.7|35.638|6.85E-07| |**Treatment:Cultivar**|8|462.9|57.9|5.733|2.92E-03| 796 **Table 9** . P-values generated from an ANOVA test using treatment, cultivar, and 797 treatment:cultivar as fixed effects and surface area value as the response variable. 798 799 |Col1|Interaction|diff|lwr|upr|padj<br>.| |---|---|---|---|---|---| |**Treatment**|Control~Moderate|-3.676|-7.630|0.279|0.070| |**Treatment**|Control~Severe|1.245|-2.609|5.099|0.678| |**Treatment**|Moderate~Severe|4.920|1.066|8.774|0.013| |**Cultivar**|Cascade~Centennial|2.024|-4.302|8.351|0.847| [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 800 |Col1|Cascade~Chinook|6656<br>.|0599<br>.|12713<br>.|0029<br>.| |---|---|---|---|---|---| ||Cascade~Comet|19.051|12.994|25.108|1.70E-06| ||Cascade~CTZ|13.955|7.898|20.012|5.29E-05| ||Centennial~Chinook|4.632|-1.425|10.689|0.174| ||Centennial~Comet|17.027|10.970|23.084|6.10E-06| ||Centennial~CTZ|11.931|5.874|17.988|2.59E-04| ||Chinook~Comet|12.395|6.620|18.170|1.10E-04| ||Chinook~CTZ|7.298|1.523|13.074|0.011| ||Comet~CTZ|-5.097|-10.872|0.679|0.095| 801 **Table 10.** Adjusted p-value and summary statistics for Tukey's post-hoc test using cultivar and 802 treatment types as the fixed effects and leaf surface area as the response variable. _diff_ 803 _represents the_ difference between the mean surface area values between groups, while _lwr_ and 804 _upr_ values represent the lower and upper endpoints of the 95% confidence interval. 805 806 807 |Col1|Df|Sum Sq|Mean Sq|F -value|Pr(>F)| |---|---|---|---|---|---| |**Treatment**|2|0.00997|0.004985|2.053|0.168| |**Cultivar**|4|0.07179|0.017947|7.391|2.47E-03| |**Treatment:Cultivar**|8|0.03016|0.00377|1.552|0.231| 808 **Table 11.** Summary statistics and p-values from an ANOVA test using leaf circularity as the 809 response variable and treatment, cultivar, and treatment:cultivar as the fixed effects. 810 811 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 812 |Col1|Interaction|diff|lwr|upr|p adj<br>.| |---|---|---|---|---|---| |**Treatment**|Control~Moderate|0.046|-0.015|0.107|0.155| |**Treatment**|Control~Severe|0.031|-0.029|0.091|0.385| |**Treatment**|Moderate~Severe|-0.015|-0.075|0.045|0.785| |**Cultivar**|Cascade~Centennial|0.016|-0.082|0.115|0.983| |**Cultivar**|Cascade~Chinook|-0.003|-0.097|0.091|1.000| |**Cultivar**|Cascade~Comet|-0.119|-0.213|-0.025|1.09E-02| |**Cultivar**|Cascade~CTZ|-0.059|-0.153|0.035|0.322| |**Cultivar**|Centennial~Chinook|-0.020|-0.114|0.074|0.961| |**Cultivar**|Centennial~Comet|-0.136|-0.230|-0.042|4.09E-03| |**Cultivar**|Centennial~CTZ|-0.076|-0.170|0.018|0.141| |**Cultivar**|Chinook~Comet|-0.116|-0.206|-0.026|9.51E-03| |**Cultivar**|Chinook~CTZ|-0.056|-0.146|0.034|0.332| |**Cultivar**|Comet~CTZ|0.060|-0.030|0.150|0.273| 813 **Table 12.** Summary statistics and adjusted p-values generated from a Tukey post-hoc test 814 exploring treatment type and cultivar as fixed effects and leaf circularity scores as the response 815 variable. 816 817 |FixedEffect<br>Cultvar|Model|SumSq|MeanSq|NumDF|DenDF|F -value|Pr(>F)| |---|---|---|---|---|---|---|---| |**Fixed Effect**<br>Cultvar|Cultivar|1.00E-04|1.80E-05|4|3797|7.90E-03|0.9999| [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) |Col1|OTU Identified<br>_|6694<br>.|0070<br>.|96|3797|30310<br>.|<2E -16| |---|---|---|---|---|---|---|---| ||Cultivar:OTU_Identified|2.136|0.006|384|3797|2.418|<2E-16| |Treatment|Treatment|0.00E+00|1.00E-05|2|3991|3.80E-03|0.9962| |Treatment|OTU_Identified|6.769|0.071|96|3991|26.677|<2E-16| |Treatment|Treatment:OTU_Identified|0.323|0.002|192|3991|0.637|1.000| |Timepoint|Timepoint|0.00E+00|4.20E-05|1|4123|1.65E-02|0.898| |Timepoint|OTU_Identified|6.7151|0.069949|96|4123|27.6131|<2E-16| |Timepoint|Timepoint:OTU|0.4271|0.007001|61|4123|2.7637|1.03E-11| 818 819 **Table 13.** Summary statistics and p-values generated from an ANOVA test of fungal OTU 820 composition identified from collected soil samples via ITS sequencing. Cultivar, treatment type, 821 and time point (start vs end) were treated as the fixed effects, while composition of identified 822 fungal OTUs was the response variable. 823 824 |FixedEffect|Model|SumSq|MeanSq|NumDF|DenDF|F -value|Pr(>F)| |---|---|---|---|---|---|---|---| |Cultvar|Cultivar|4.33E-06|1.08E-06|4|306|1.94|0.1039826| |Cultvar|OTU_Identified|4.04E-05|8.09E-06|5|306|14.49|9.63E-13| |Cultvar|Cultivar:OTU_Identified|3.03E-05|1.51E-06|20|306|2.71|1.41E-04| |Treatment|Treatment|3.02E-07|1.51E-07|2|318|0.24|0.79| |Treatment|OTU_Identified|4.34E-05|8.69E-06|5|318|13.55|5.37E-12| |Treatment|Treatment:OTU_Identified|1.33E-06|1.33E-07|10|318|0.21|0.996| |Timepoint|Timepoint|1.21E-06|1.21E-06|1|324|2.03|0.155| |Timepoint|OTU_Identified|4.41E-05|8.82E-06|5|324|14.86|3.95E-13| |Timepoint|Timepoint:OTU|1.18E-05|2.37E-06|5|324|3.98|1.61E-03| [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 825 826 **Table 14.** Summary statistics and p-values generated from ANOVA test of archaeal OTU 827 composition identified from collected soil samples via 16Ssequencing. Cultivar, treatment type, 828 and time point (start vs end) were treated as the fixed effects, while composition of identified 829 fungal OTU was the response variable. 830 831 |FixedEffect|Model|SumSq|MeanSq|NumDF|DenDF|F -value|Pr(>F)| |---|---|---|---|---|---|---|---| |Cultvar|Cultivar|1.30E-04|3.22E-05|4|8981|0.27|0.8984| |Cultvar|OTU_Identified|2.66E+00|1.53E-02|174|8981|127.21|<2E-16| |Cultvar|Cultivar:OTU_Identified|3.17E-01|4.55E-04|696|8981|3.79|<2E-16| |Treatment|Treatment|4.00E-05|2.04E-05|2|9331|0.14|0.8662| |Treatment|OTU_Identified|2.68E+00|1.54E-02|174|9331|108.26|<2E-16| |Treatment|Treatment:OTU_Identified|6.75E-02|1.94E-04|348|9331|1.36|1.17E-05| |Timepoint|Timepoint|2.00E-05|2.33E-05|1|9506|0.16|0.686| |Timepoint|OTU_Identified|2.67E+00|1.53E-02|174|9506|108.11|<2E-16| |Timepoint|Timepoint:OTU|4.54E-02|2.61E-04|174|9506|1.84|2.05E-10| 832 833 **Table 15** . Summary statistics and p-values generated from an ANOVA test of bacterial OTU 834 composition identified from collected soil samples via 16S sequencing. Cultivar, treatment type, 835 and time point (start vs end) were treated as the fixed effects, while composition of identified 836 fungal OTUs was the response variable. 837 |Cultivar|OTU|Estimate|StdError|df|t -value|Pr(>|t|)| |---|---|---|---|---|---|---| |**Centennial**|Bacillaceae|-1.37E-02|6.93E-03|8981|-1.982|4.76E-02| |**Centennial**|Nocardioidaceae|-1.08E-03|6.93E-03|8981|-0.156|8.76E-01| |**Centennial**|Paenibacillaceae|-2.87E-02|6.93E-03|8981|-4.14|3.51E-05| |**Centennial**|Planctomycetaceae|1.06E-03|6.93E-03|8981|0.153|0.878298| |**Centennial**|Rhodothermaceae|-1.16E-03|6.00E-03|8981|-0.192|8.47E-01| |**Centennial**|Streptomycetaceae|1.30E-02|6.93E-03|8981|1.882|5.99E-02| [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) |Col1|Streptosporangiaceae|132E 03<br>- . -|693E 03<br>. -|8981|019<br>- .|849E 01<br>. -| |---|---|---|---|---|---|---| |**Chinook**|Bacillaceae|-8.70E-02|6.64E-03|8981|-13.109|2.00E-16| |**Chinook**|Nocardioidaceae|-8.49E-03|6.64E-03|8981|-1.279|2.01E-01| |**Chinook**|Paenibacillaceae|-3.61E-02|6.64E-03|8981|-5.435|5.64E-08| |**Chinook**|Planctomycetaceae|1.35E-02|6.64E-03|8981|2.038|4.16E-02| |**Chinook**|Rhodothermaceae|2.41E-03|5.75E-03|8981|0.42|6.74E-01| |**Chinook**|Streptomycetaceae|2.51E-02|6.64E-03|8981|3.789|1.53E-04| |**Chinook**|Streptosporangiaceae|-1.80E-02|6.64E-03|8981|-2.705|6.84E-03| |**Comet**|Bacillaceae|-2.05E-02|6.64E-03|8981|-3.096|1.97E-03| |**Comet**|Nocardioidaceae|9.13E-03|6.64E-03|8981|1.376|1.69E-01| |**Comet**|Paenibacillaceae|-3.97E-02|6.64E-03|8981|-5.988|2.21E-09| |**Comet**|Planctomycetaceae|9.25E-03|6.64E-03|8981|1.393|1.64E-01| |**Comet**|Rhodothermaceae|9.89E-04|5.75E-03|8981|0.172|8.63E-01| |**Comet**|Streptomycetaceae|4.73E-03|6.64E-03|8981|0.712|4.76E-01| |**Comet**|Streptosporangiaceae|-2.14E-02|6.64E-03|8981|-3.225|1.26E-03| |**CTZ**|Bacillaceae|1.13E-01|6.64E-03|8981|17.053|2.00E-16| |**CTZ**|Nocardioidaceae|1.10E-02|6.64E-03|8981|1.653|9.85E-02| |**CTZ**|Paenibacillaceae|-4.44E-02|6.64E-03|8981|-6.693|2.32E-11| |**CTZ**|Planctomycetaceae|4.95E-03|6.64E-03|8981|0.746|4.56E-01| |**CTZ**|Rhodothermaceae|5.14E-03|5.75E-03|8981|0.894|3.72E-01| |**CTZ**|Streptomycetaceae|8.24E-03|6.64E-03|8981|1.242|2.14E-01| |**CTZ**|Streptosporangiaceae|-3.04E-02|6.64E-03|8981|-4.584|4.61E-06| 838 **Table 16.** Summary statistics and p-values associated with each cultivar type for bacterial 839 OTU’s identified as significant by ANOVA testing. [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 840 |OTU|Estimate|StdError|df|t -value|Pr(>|t|)| |---|---|---|---|---|---| |Bacillaceae|1.87E-01|3.98E-03|9331|47.053|2.00E-16| |Hyphomicrobiaceae|1.14E-02|3.98E-03|9331|2.876|4.03E-03| |Nocardioidaceae|2.09E-02|3.98E-03|9331|5.258|1.49E-07| |Paenibacillaceae|6.61E-02|3.98E-03|9331|16.637|2.00E-16| |Planctomycetaceae|1.73E-02|3.98E-03|9331|4.352|1.36E-05| |Staphylococcaceae|5.18E-02|3.98E-03|9331|13.028|2.00E-16| |Streptosporangiaceae|2.31E-02|3.98E-03|9331|5.81|6.46E-09| |Xanthobacteraceae|9.28E-03|3.98E-03|9331|2.335|0.01958| 841 **Table 17.** Table of eight bacterial OTU’s detected from the rhizosphere and identified as 842 displaying a significant change in percent abundance between the start and end of the 12-week 843 growing period. The table displays summary statistics and associated p-values generated from 844 a linear mixed model using treatment and OTU as the fixed effects and percent abundance as 845 the response variable. 846 **References:** 847 Acosta-Rangel, A., Rechcigl, J., Bollin, S., Deng, Z., & Agehara, S. (2021). Hop 848 (“Humulus lupulus” L.) phenology, growth, and yield under subtropical climatic 849 conditions: Effects of cultivars and crop management. _Australian Journal of Crop_ 850 _Science_, _15_ (5), 764–772. https://doi.org/10.3316/informit.167653807817601 851 Adebayo, A. A., Kutu, F. R., Ndolo, D., Orozco-Mosqueda, M. del C., Santoyo, G., & 852 Babalola, O. O. (2026). Soil Microbiota and Rhizosphere Dynamics: Ecological 853 Mechanisms Driving Crop Stress Adaptation in Climate-Smart Agriculture. _Food and_ 854 _Energy Security_, _15_ (4), e70286. https://doi.org/10.1002/fes3.70286 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 855 Agraso-Otero, A., Cancela, J. J., Fandiño, M., Rebolledo-Leiva, R., & González-García, 856 S. (2026). Assessing environmental impacts and ecosystem services of Hops crop in 857 Galicia, NW Spain: Critical contributors for sustainable cultivation strategies. 858 _Environmental Science and Pollution Research_, _33_ (11), 5133–5147. 859 https://doi.org/10.1007/s11356-026-37623-0 860 Arora, N. K. (2019). Impact of climate change on agriculture production and its 861 sustainable solutions. _Environmental Sustainability_, _2_ (2), 95–96. 862 https://doi.org/10.1007/s42398-019-00078-w 863 Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects 864 Models Using lme4. _Journal of Statistical Software_, _67_, 1–48. 865 https://doi.org/10.18637/jss.v067.i01 866 Bibi, F., & Rahman, A. (2023). An Overview of Climate Change Impacts on Agriculture 867 and Their Mitigation Strategies. _Agriculture_, _13_ (8), 1508. 868 https://doi.org/10.3390/agriculture13081508 869 Brewers Association. (2025). National Beer Sales & Production Data. 870 https://www.brewersassociation.org/statistics-and-data/national-beer-stats/. Accessed 871 October 2026. 872 Burns, A., Kerou, M., Zak, D., Schleper, C., Urich, T., & Wang, H. (2025). Ammonia 873 oxidizing archaea and bacteria respond dynamically to drought in rewetted fen 874 peatlands. _Applied Soil Ecology_, _214_, 106395. 875 https://doi.org/10.1016/j.apsoil.2025.106395 876 Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, 877 S. P. (2016). DADA2: High resolution sample inference from Illumina amplicon data. 878 _Nature Methods_, _13_ (7), 581–583. https://doi.org/10.1038/nmeth.3869 879 Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, 880 E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., Huttley, G. A., Kelley, S. T., [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 881 Knights, D., Koenig, J. E., Ley, R. E., Lozupone, C. A., McDonald, D., Muegge, B. D., 882 Pirrung, M., … Knight, R. (2010). QIIME allows analysis of high-throughput community 883 sequencing data. _Nature Methods_, _7_ (5), 335–336. https://doi.org/10.1038/nmeth.f.303 884 Castro, P., Luz, E., & Moreira, G. (2021). Dataset for Hop varieties classification. _Data in_ 885 _Brief_, _38_, 107312. https://doi.org/10.1016/j.dib.2021.107312 886 Curá, J. A., Franz, D. R., Filosofía, J. E., Balestrasse, K. B., & Burgueño, L. E. (2017). 887 Inoculation with Azospirillum sp. And Herbaspirillum sp. Bacteria Increases the 888 Tolerance of Maize to Drought Stress. _Microorganisms_, _5_ (3), 41. 889 https://doi.org/10.3390/microorganisms5030041 890 Eftekhari, M. S. (2022). Impacts of Climate Change on Agriculture and Horticulture. In S. 891 A. Bandh (Ed.), _Climate Change: The Social and Scientific Construct_ (pp. 117–131). 892 Springer International Publishing. https://doi.org/10.1007/978-3-030-86290-9_8 893 Eriksen, R. L., Rutto, L. K., Dombrowski, J. E., & Henning, J. A. (2020). Photosynthetic 894 Activity of Six Hop (Humulus lupulus L.) Cultivars under Different Temperature 895 Treatments. _HortScience_, _55_ (4), 403–409. https://doi.org/10.21273/HORTSCI14580-19 896 Feng, X., Liu, C., Xie, F., Lu, J., Chiu, L. S., Tintera, G., & Chen, B. (2019). Precipitation 897 characteristic changes due to global warming in a high-resolution (16 km) ECMWF 898 simulation. _Quarterly Journal of the Royal Meteorological Society_, _145_ (718), 303–317. 899 https://doi.org/10.1002/qj.3432 900 Francioli, D., Kampouris, I. D., Kuhl-Nagel, T., Babin, D., Sommermann, L., Behr, J. H., 901 Chowdhury, S. P., Zrenner, R., Moradtalab, N., Schloter, M., Geistlinger, J., Ludewig, U., 902 Neumann, G., Smalla, K., & Grosch, R. (2025). Microbial inoculants modulate the 903 rhizosphere microbiome, alleviate plant stress responses, and enhance maize growth at 904 field scale. _Genome Biology_, _26_, 148. https://doi.org/10.1186/s13059-025-03621-7 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 905 Hightower, A. T., Chitwood, D. H., & Josephs, E. B. (2024). Herbarium specimens reveal 906 links between leaf shape of Capsella bursa � pastoris and climate. _American Journal of_ 907 _Botany_, _111_ (11), e16435. https://doi.org/10.1002/ajb2.16435 908 Jha, C. K., & Saraf, M. (2015). Plant growth promoting Rhizobacteria (PGPR): A review 909 RESEARCH · JUNE 2015. _Journal of Agricultural Research and Development_, _5_ (2), 910 0108–0119. https://doi.org/10.13140/RG.2.1.5171.2164 911 Kassambara, A. (2026). _rstatix: Pipe-Friendly Framework for Basic Statistical Tests_ 912 (Version 1.1.0) [Computer software]. https://cran.r 913 project.org/web/packages/rstatix/index.html 914 King, M., Altdorff, D., Li, P., Galagedara, L., Holden, J., & Unc, A. (2018). Northward shift 915 of the agricultural climate zone under 21st-century global climate change. _Scientific_ 916 _Reports_, _8_ (1), 7904. https://doi.org/10.1038/s41598-018-26321-8 917 Konapala, G., Mishra, A. K., Wada, Y., & Mann, M. E. (2020). Climate change will affect 918 global water availability through compounding changes in seasonal precipitation and 919 evaporation. _Nature Communications_, _11_ (1), 3044. https://doi.org/10.1038/s41467-020 920 16757-w 921 Korpelainen, H., & Pietiläinen, M. (2021). Hop (Humulus lupulus L.): Traditional and 922 Present Use, and Future Potential. _Economic Botany_, _75_ (3), 302–322. 923 https://doi.org/10.1007/s12231-021-09528-1 924 Kristy, B., Carrell, A. A., Johnston, E., Cumming, J. R., Klingeman, D. M., Gwinn, K., 925 Syring, K. C., Skalla, C., Emrich, S., & Cregger, M. A. (2022). Chronic Drought 926 Differentially Alters the Belowground Microbiome of Drought-Tolerant and Drought 927 Susceptible Genotypes of Populus trichocarpa. _Phytobiomes Journal_, _6_ (4), 317–330. 928 https://doi.org/10.1094/PBIOMES-12-21-0076-R 929 Kumar, M., Mishra, S., Dixit, V., Kumar, M., Agarwal, L., Chauhan, P. S., & Nautiyal, C. 930 S. (2016). Synergistic effect of Pseudomonas putida and Bacillus amyloliquefaciens [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 931 ameliorates drought stress in chickpea (Cicer arietinum L.). _Plant Signaling & Behavior_, 932 _11_ (1), e1071004. https://doi.org/10.1080/15592324.2015.1071004 933 Kuo, S. (1996) Phosphorus. Pages 869-920 in J.M Bartels (ed.) Methods of Soil Analysis 934 Part 3:Chemical Methods. Soils Science Society of America, Madison, WI 935 Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest Package: 936 Tests in Linear Mixed Effects Models. _Journal of Statistical Software_, _82_, 1–26. 937 https://doi.org/10.18637/jss.v082.i13 938 Laishram, B., Devi, O. R., Dutta, R., Senthilkumar, T., Goyal, G., Paliwal, D. K., Panotra, 939 N., & Rasool, A. (2024). Plant-microbe interactions: PGPM as microbial 940 inoculants/biofertilizers for sustaining crop productivity and soil fertility. _Current Research_ 941 _in Microbial Sciences_, _8_, 100333. https://doi.org/10.1016/j.crmicr.2024.100333 942 Lenth R, Piaskowski J (2026). emmeans: Estimated Marginal Means, aka Least-Squares 943 Means. R package version 2.0.5, https://rvlenth.github.io/emmeans/ 944 Lim, J.-H., & Kim, S.-D. (2013). Induction of Drought Stress Resistance by Multi 945 Functional PGPR Bacillus licheniformis K11 in Pepper. _The Plant Pathology Journal_, 946 _29_ (2), 201–208. https://doi.org/10.5423/PPJ.SI.02.2013.0021 947 Malhi, G. S., Kaur, M., & Kaushik, P. (2021). Impact of Climate Change on Agriculture 948 and Its Mitigation Strategies: A Review. _Sustainability_, _13_ (3), 1318. 949 https://doi.org/10.3390/su13031318 950 McAdam, E. L., Freeman, J. S., Whittock, S. P., Buck, E. J., Jakse, J., Cerenak, A., 951 Javornik, B., Kilian, A., Wang, C.-H., Andersen, D., Vaillancourt, R. E., Carling, J., 952 Beatson, R., Graham, L., Graham, D., Darby, P., & Koutoulis, A. (2013). Quantitative 953 trait loci in hop (Humulus lupulusL.) reveal complex genetic architecture underlying 954 variation in sex, yield and cone chemistry. _BMC Genomics_, _14_ (1), 360. 955 https://doi.org/10.1186/1471-2164-14-360 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 956 McElwee-Adame, A., Esplin-Stout, R., Mugoya, T., Vourlitis, G., Welch, N., Henning, J., 957 Afram, K., Jeshvaghane, M. A., Bingham, N., Dockter, A., Eslava, J., Gil, G., Mergens, 958 J., Mohamed, A., Nguyen, T., Noor, F., Salcedo, N., & Sethuraman, A. (2025). 959 Evolutionary History and Rhizosphere Microbial Community Composition in 960 Domesticated Hops (Humulus lupulus L.). _Molecular Ecology_, _34_ (24), e17769. 961 https://doi.org/10.1111/mec.17769 962 Miranda, K.M., Espey, M.G. and Wink, D.A., 2001. A rapid, simple spectrophotometric 963 method for simultaneous detection of nitrate and nitrite. Nitric oxide, 5(1), pp.62-71. 964 Mohanta, T. K., Mohanta, Y. K., Kaushik, P., & Kumar, J. (2023). Physiology, genomics, 965 and evolutionary aspects of desert plants. _Journal of Advanced Research_, _58_, 63–78. 966 https://doi.org/10.1016/j.jare.2023.04.019 967 Mozny, M., Tolasz, R., Nekovar, J., Sparks, T., Trnka, M., & Zalud, Z. (2009). The 968 impact of climate change on the yield and quality of Saaz hops in the Czech Republic. 969 _Agricultural and Forest Meteorology_, _149_ (6), 913–919. 970 https://doi.org/10.1016/j.agrformet.2009.02.006 971 Mozny, M., Trnka, M., Vlach, V., Zalud, Z., Cejka, T., Hajkova, L., Potopova, V., 972 Semenov, M. A., Semeradova, D., & Büntgen, U. (2023). Climate-induced decline in the 973 quality and quantity of European hops calls for immediate adaptation measures. _Nature_ 974 _Communications_, _14_ (1), 6028. https://doi.org/10.1038/s41467-023-41474-5 975 Muhammad, M., Wahab, A., Waheed, A., Hakeem, K. R., Mohamed, H. I., Basit, A., 976 Toor, M. D., Liu, Y.-H., Li, L., & Li, W.-J. (2025). Navigating Climate Change: Exploring 977 the Dynamics Between Plant–Soil Microbiomes and Their Impact on Plant Growth and 978 Productivity. _Global Change Biology_, _31_ (2), e70057. https://doi.org/10.1111/gcb.70057 979 Mulvaney, R.L., 1996. Nitrogen—inorganic forms. Methods of soil analysis: Part 3 980 Chemical methods, 5, pp.1123-1184. [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 981 Murakami, A., Darby, P., Javornik, B., Pais, M. S. S., Seigner, E., Lutz, A., & Svoboda, 982 P. (2006). Molecular phylogeny of wild Hops, Humulus lupulus L. _Heredity_, _97_ (1), 66–74. 983 https://doi.org/10.1038/sj.hdy.6800839 984 Nakawuka, P., Peters, T. R., Kenny, S., & Walsh, D. (2017). Effect of deficit irrigation on 985 yield quantity and quality, water productivity and economic returns of four cultivars of 986 hops in the Yakima Valley, Washington State. _Industrial Crops and Products_, _98_, 82–92. 987 https://doi.org/10.1016/j.indcrop.2017.01.037 988 Peterson, R. A., & Cavanaugh, J. E. (2020). Ordered quantile normalization: A 989 semiparametric transformation built for the cross-validation era. _Journal of Applied_ 990 _Statistics_, _47_ (13–15), 2312–2327. https://doi.org/10.1080/02664763.2019.1630372 991 R Core Team (2026). R: A Language and Environment for Statistical Computing. R 992 Foundation for Statistical Computing, Vienna, Austria. 993 Rashid, U., Yasmin, H., Hassan, M. N., Naz, R., Nosheen, A., Sajjad, M., Ilyas, N., 994 Keyani, R., Jabeen, Z., Mumtaz, S., Alyemeni, M. N., & Ahmad, P. (2022). Drought 995 tolerant Bacillus megaterium isolated from semi-arid conditions induces systemic 996 tolerance of wheat under drought conditions. _Plant Cell Reports_, _41_ (3), 549–569. 997 https://doi.org/10.1007/s00299-020-02640-x 998 Rossini, F., Virga, G., Loreti, P., Iacuzzi, N., Ruggeri, R., & Provenzano, M. E. (2021). 999 Hops (Humulus lupulus L.) as a Novel Multipurpose Crop for the Mediterranean Region 1000 of Europe: Challenges and Opportunities of Their Cultivation. _Agriculture_, _11_ (6), 484. 1001 https://doi.org/10.3390/agriculture11060484 1002 Ruggeri, R., Rossini, F., Roberto, S. R., Sato, A. J., Loussert, P., Rutto, L. K., & 1003 Agehara, S. (2024). Development of hop cultivation in new growing areas: The state of 1004 the art and the way forward. _European Journal of Agronomy_, _161_, 127335. 1005 https://doi.org/10.1016/j.eja.2024.127335 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 1006 Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 1007 years of image analysis. _Nature Methods_, _9_ (7), 671–675. 1008 https://doi.org/10.1038/nmeth.2089 1009 Sheteiwy, M. S., Elgawad, H. A., Xiong, Y.-C., Macovei, A., Brestic, M., Skalicky, M., 1010 Shaghaleh, H., Hamoud, Y. A., & El-Sawah, A. M. (2021). Inoculation with Bacillus 1011 amyloliquefaciens and mycorrhiza confers tolerance to drought stress and improve seed 1012 yield and quality of soybean plant. _Physiologia Plantarum_, _172_ (4), 2153–2169. 1013 https://doi.org/10.1111/ppl.13454 1014 Sims, J.R. and Haby, V.A., 1971. Simplified colorimetric determination of soil organic 1015 matter. Soil Science, 112(2), pp.137-141 1016 Sood, G., Kaushal, R., & Sharma, M. (2020). Significance of inoculation with Bacillus 1017 subtilis to alleviate drought stress in wheat (Triticum aestivum L.). _Vegetos_, _33_ (4), 782– 1018 792. https://doi.org/10.1007/s42535-020-00149-y 1019 Tugbaeva, A. S., Ermoshin, A. A., Shiryaev, G. I., & Kiseleva, I. S. (2025). Microbiome of 1020 the Soil and Rhizosphere of the Halophyte Spergularia marina (L.) Griseb in the Saline 1021 Sites of Lake Kurgi, the South Urals: Metagenomic Analysis. _Microbiology Research_, 1022 _16_ (3), 64. https://doi.org/10.3390/microbiolres16030064 1023 United States Department of Agriculture- National Agricultural Statistical Services. 1024 (2024). National Hops Report 2024. 1025 https://www.nass.usda.gov/Statistics_by_State/Regional_Office/Northwest/includes/Publ 1026 ications/Hops/2024/hops1224.pdf. Accessed October 2026. 1027 Vardharajula, S., Zulfikar Ali, S., Grover, M., Reddy, G., & Bandi, V. (2011). Drought 1028 tolerant plant growth promoting Bacillus spp.: Effect on growth, osmolytes, and 1029 antioxidant status of maize under drought stress. _Journal of Plant Interactions_, _6_ (1), 1– 1030 14. https://doi.org/10.1080/17429145.2010.535178 [bioRxiv preprint doi: https://doi.org/10.64898/2026.11.03.764026; this version posted November 4, 2026. The copyright holder for this preprint](https://doi.org/10.64898/2026.11.03.764026) (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made [available under a CC-BY-ND 4.0 International license.](http://creativecommons.org/licenses/by-nd/4.0/) 1031 Varsamis, G., Adamidis, G. C., Merou, T., Takos, I., Tseniklidou, K., Dimitrakopoulos, P. 1032 G., & Papageorgiou, A. C. (2022). Changes in Watering Frequency Stimulate 1033 Differentiated Adaptive Responses among Seedlings of Different Beech Populations. 1034 _Biology_, _11_ (2), 306. https://doi.org/10.3390/biology11020306 1035 Vose, R. S., Huang, B., Yin, X., Arndt, D., Easterling, D. R., Lawrimore, J. H., Menne, M. 1036 J., Sanchez � Lugo, A., & Zhang, H. M. (2021). Implementing Full Spatial Coverage in 1037 NOAA’s Global Temperature Analysis. _Geophysical Research Letters_, _48_ (4), 1038 e2020GL090873. https://doi.org/10.1029/2020GL090873 1039 Wickham H (2016). _ggplot2: Elegant Graphics for Data Analysis_ . Springer-Verlag New 1040 York. ISBN 978-3-319-24277-4. https://ggplot2.tidyverse.org 1041 Wiszpolski, P. S., & Stolarski, M. J. (2026). The Role of the Rhizosphere, Endophytes, 1042 and the Influence of Plant-Growth-Promoting Bacteria: Take the Cannabis Microbiome 1043 as an Example. _International Journal of Molecular Sciences_, _27_ (11), 4802. 1044 https://doi.org/10.3390/ijms27114802 1045 Yuan, X., Li, S., Chen, J., Yu, H., Yang, T., Wang, C., Huang, S., Chen, H., & Ao, X. 1046 (2024). Impacts of Global Climate Change on Agricultural Production: A Comprehensive 1047 Review. _Agronomy_, _14_ (7), 1360. https://doi.org/10.3390/agronomy14071360