Grazing is known to have long-term effects on soil biology and vegetation, but little is known about how grazing impacts the soil microbial community on a short timescale.Understanding how above-and below-ground processes respond to grazing disturbance is an important step to understanding how ecosystem services shift following grazing impacts. To address this knowledge gap, a grazing trial was conducted over two growing seasons, and soil microbial community data, vegetation biomass, and soil labile nutrients were quantified immediately after through four weeks following different grazing severities. This research is the first to show that both high-intensity, short-duration grazing and low-intensity, medium-duration grazing cause detectable changes in the soil microbial community and labile soil nutrients as soon as 24 hours after grazing. Additional research questions are addressed by summarizing the current state of recent grazing research utilizing molecular methods, and by testing a novel method to use ground-level spectral imagery to measure vegetation biomass opposed to traditional methods. Overall, this research contributes valuable knowledge to the field of soil microbial ecology and land management. The results and management recommendations were disseminated to producers via Extension outreach and to undergraduate students at University of Wyoming as part of soil health curriculum. To convey results to the scientific community, a poster was presented at the Ecological Society of America conference, and we will submit manuscripts to peer-reviewed journals.
- Do soil microbial dynamics differ in response to intensive grazing regimes as opposed to light grazing and grazing exclusions?
- Is there an immediate response of the soil microbial community to high-intensity or low-intensity grazing compared to grazing exclusions?
- What is the effect of intensive defoliation on vegetation recovery growth?
- Will an increase in microbial dynamics affect vegetation recovery growth?
We hypothesized different soil microbiological responses to different grazing management strategies. To test this hypothesis, we conducted a grazing trial over two growing seasons that compared high-intensity,short-duration grazing to low-intensity, medium-duration grazing and a no-grazing control in an irrigated meadow dominated by introduced forage species. Vegetation biomass, soil labile nutrients and the soil microbial community activity, diversity, and function were assessed before grazing and 24 hours through four weeks immediately after grazing. We utilized modern high-throughput molecular techniques to analyze the soil microbial community, including extracellular enzymatic assays and 16S and ITS amplicon sequencing. This is the first study that assesses the immediate response of the soil ecosystem to grazing disturbance.
While exploring the potential use of spectral imaging to replace traditional methods of measuring aboveground biomass, we found that while the method has potential, the relationship between red, blue, and near-infrared light wavelengths and vegetation biomass is not valid in high-production systems, i.e. with a dried vegetation biomass over 2000 kg/acre. Ground-level spectral imaging with commercially available cameras has potential to replace time-costly traditional methods of monitoring forage quantity, but a more available workflow is needed to make data analysis easier for land managers, and a different spectral index is needed that does not saturate at high vegetation biomass and canopy cover. This is beneficial to land managers who are interested in monitoring small-scales changes in vegetation, which is highly recommended when utilizing high-intensity grazing methods.
We found that labile soil C and N increased following the low-density grazing while C-cycling extracellular enzymatic activities increased in response to high-density grazing but total extracellular enzymatic activity profiles were strongly affected by sampling time. The soil fungal community structure was strongly affected by the interaction of sampling time and grazing treatment, while the soil bacterial community was largely resilient to change. We found evidence of seasonal influences on soil chemical and microbial parameters, even over the total sampling time of five weeks during the growing season. These results highlight the complexity of soil microbiota and the difficulty of translating ecological dynamics into real-world management recommendations. Although our results showed that high-intensity, short-duration grazing has an immediate impact on soil microbial dynamics, it is difficult to ascribe a management quality to those changes, i.e. if the changes are beneficial to ecosystem services that matter to producers. However, these results confirm that producers have an ability to impact soil biological functioning with management changes which holds importance for producers that may be interested in changing grazing styles on irrigated pastures.
This research found that the soil microbial community and soil labile nutrients respond as soon as 24 hours following grazing, and seasonal and spatial heterogeneity has a large impact on soil microbial activity and functioning. These results have implications for future experimental designs and future research and illustrate a need for research on better functional understanding of the soil microbial community.This study is the first to detect pulses in soil microbial activity,diversity, and function within 24 hours of grazing disturbance, but future research is needed to assign functional significance to taxonomic shifts. This need is reflected in current research findings on grazing impacts on the soil microbial community, which indicate that climate, soil type, and grazing intensity drive the severity of impacts on the soil microbial community. In order to translate research to management recommendations, research should begin to link phylogeny to function. Soil microbial richness and diversity is relatively resilient to grazing impacts,but the structure of the community can respond, either on very short timescales as we have shown here, or on longer time scales, as has been previously documented.
- Quantify the effects of high-intensity grazing compared to conventional grazing on soil physical, chemical, and biological parameters over a growing season.
- Detect the flux of microbial community dynamics immediately following high-intensity grazing events, contrasted to fluxes that occur during conventional, low-intensity grazing.
- Document the immediate response of vegetation to homogenous, intensive defoliation and to heterogeneous, moderate defoliation.
- Synthesize the interactions of above-and below ground processes in response to high-intensity, short-duration grazing.
Grazing trials were conducted during the summer growing seasons of 2017 and 2018. The field site is located at the University of Wyoming’s Laramie Agricultural Experiment Station in Laramie, Wyoming (41°18’13.5”N, 105°38’24.4”W), and is composed of twelve adjacent half acre (~0.202 ha) paddocks along WY-230 which have historically been flood irrigated and irregularly grazed. Three grazing treatments were replicated four times in a randomized complete block design. The treatments were: high intensity, short-duration grazing (HDG); low intensity,medium-duration grazing (LDG), and no grazing (NG). Soil, vegetation, and spectral imaging samples were collected at 4 time points: 1 week before grazing (PRE), 24 hours after grazing began (24H), 1 week after grazing began (1WK), and 4 weeks after grazing began (4WK). The stocking density for both years for the HDG treatment was planned as 50,000 animal units (AU; 1,000 lbs.) acre -1 (~20,234 AU hectare -1 ) and the LDG treatment was planned as 2,000 AU acre -1 (~ 809 AU hectare -1 ).
Rising plate pasture meter (RPM) measurements were used for estimation of vegetation biomass as a nondestructive alternative to traditional clipped dry weight biomass. Traditional clipped and dried biomass samples were also collected to compare the accuracy of RPM measurements compared to traditional in an irrigated, high-production pasture.
Basic soil analyses included gravimetric soil moisture, pH, and soil labile carbon (C) and nitrogen (N) pools. Activities of carbon, nitrogen, and phosphorus-cycling soil extracellular enzymes were measured via extracellular enzymatic assays. The soil microbial community structure was analyzed via amplicon sequencing of the 16S ribosomal subunit (bacterial) and and the ITS region (fungal) DNA.
A 16MP spectral camera (AgroCam Pro NDVI, AgroCam, Norway) was mounted on a stand at 1m height from the ground. Five photos were taken per plot at random points during each sampling time. The photos were taken at the same azimuth orientation to minimize shadow interference, and at the same time of day (between 8:00 AM and 10:00 AM) to minimize variance from the sun’s position. Every sampling day had minimal or no cloud cover. Post-processing was performed in Program R, and five spectral vegetation indices were chosen to analyze (Table 1). All data was aggregated per plot (n=60 per year). Biomass measurements were log-transformed and index measurements were transformed with Tukey’s Ladder of Powers using the rcompanion package in R. A general linear mixed model was performed to regress each spectral data index to biomass measurements.
|Blue-wide dynamic range index||BWDR||Gitelson, 2004|
|Blue-normalized difference vegetation index||BNDVI||Yang et al., 2004|
|Green-normalized difference vegetation index||GNDVI||
Gitelson et al., 1996
|Chlorophyll index-Green||CHIG||Gitelson, 2003|
|Simple ratio green||SRAG||Daughtry et al., 2000|
Statistical analysis was performed in Program R. All soil physicochemical variables and vegetation data were analyzed individually using general linear models (GLM) with grazing treatment and time after grazing as fixed effects and block as a random effect. Because of the heterogeneous nature of soil, data was converted to percent change from the pre-grazing baseline at the plot level and log-transformed to fit the assumptions of linear regression. If the GLM indicated significance (p< 0.05), pairwise comparisons were made via estimated marginal means (aka least-squares means) using the emmeans package in R.
Extracellular enzymatic data was transformed and analyzed via general linear models as described above. Effects of treatment and time after grazing on the structure of total enzymatic profiles were analyzed with a PERMANOVA using the adonis function in the vegan R package. Effects of treatment and time after grazing on the soil microbial community structure was assessed by performing a PERMANOVA with the adonis function in the vegan package with soil type (bulk or rhizospheric), treatment, time after grazing, and the interaction of treatment & time as factors.
Educational & Outreach Activities
During the formation of the experimental design of this project, input was solicited from the attendees of Wyoming Stockgrower’s Association Range Soil Health Workshop in Buffalo, WY. Participants were shown a Powerpoint presentation of the objectives and experimental design of the project, and verbal input was sought as to how the project could be more applicable to the participants. In November 2017, I presented a poster presentation at the Wyoming Weed & Pest Conference in Sheridan, WY. The poster explained the background, objectives, and materials & methods of the project in layperson’s terms and was designed to educate the conference attendees on the poster. In April 2018, a bulletin was published in Field Days Bulletin: University of Wyoming that is freely available online to the public, and distributed to Wyoming producers who are interested in learning about projects happening around Wyoming’s agricultural research centers.
Several outreach publications and activities are in progress or planned for the future. This project is being incorporated into an active learning 3D soils modules, where undergraduate students can manipulate management decisions, such as grazing intensity, and watch how the soil changes. A pilot study is underway and will be presented to students during the fall semester of 2018. Once final results from the summer 2018 grazing trial are analyzed a secondary extension bulletin will be published, and the results will also be shared during a future Wyoming Stockgrower’s Association meeting, where producers will be asked to share their opinion and feedback on the study. Results will be disseminated via scientific journal publications and academic conference presentations as well.
This project was the first to our knowledge to show that the soil microbial community responds within as little as 24 hours following disturbance. This finding has implications both for future research and the field of sustainable agriculture. Future experimental designs should consider short-term impacts on the soil microbial community and seek to understand the consequences on functional outcomes, such as forage production and quality. Since we have now found that the soil microbial community responds on very short time scales, there should be more research on how intensive grazing management has short-term effects on the soil microbial community.
One of the most important findings of this project was the scale at which human management decisions have immediate impacts on the soil ecosystem. Managing for soil sustainability is a critical component of sustainable agriculture and we hope to see the impact of our work in future research.
My advisor and I gained appreciation for the value of having producer input while designing an experiment. We worked with Dr. Derek Scasta, the rangeland extension specialist at the University of Wyoming, to target realistic management goals and monitoring. We also gained appreciation for the difficulty of transcribing a plot-level research study to management prescriptions.
One of our biggest challenges during the completion of this project was working through the inherent spatial heterogeneity of the soil habitat and soil microbiota. This is a common problem throughout the soil science field, and definitely an area that warrants more research to be able to make sustainable agricultural management recommendations based on statistically sound scientific experiments.