Kellogg Biological Station Research Articles

The LTAR Cropland Common Experiment at the Kellogg Biological Station.

The Kellogg Biological Station Long-term Agroecosystem Research site (KBS LTAR) joined the national LTAR Network in 2015 to represent a northeast portion of the North Central Region, extending across 76,000 km2 of southern Michigan and northern Indiana. Regional cropping systems are dominated by corn (Zea mays)-soybean (Glycine max) rotations managed with conventional tillage, industry-average rates of fertilizer and pesticide inputs uniformly applied, few cover crops, and little animal integration. In 2020, KBS LTAR initiated the Aspirational Cropping System Experiment as part of the LTAR Common Experiment, a co-production model wherein stakeholders and researchers collaborate to advance transformative change in agriculture. The Aspirational (ASP) cropping system treatment, designed by a team of agronomists, farmers, scientists, and other stakeholders, is a five-crop rotation of corn, soybean, winter wheat (Triticum aestivum), winter canola (Brassicus napus), and a diverse forage mix. All phases are managed with continuous no-till, variable rate fertilizer inputs, and integrated pest management to provide benefits related to economic returns, water quality, greenhouse gas mitigation, soil health, biodiversity, and social well-being. Cover crops follow corn and winter wheat, with fall-planted crops in the rotation providing winter cover in other years. The experiment is replicated with all rotation phases at both the plot and field scales and with perennial prairie strips in consistently low-producing areas of ASP fields. The prevailing practice (or Business as usual [BAU]) treatment mirrors regional prevailing practices as revealed by farmer surveys. Stakeholders and researchers evaluate the success of the ASP and BAU systems annually and implement management changes on a 5-year cycle.

Machine learning reveals dynamic controls of soil nitrous oxide emissions from diverse long-term cropping systems.

Soil nitrous oxide (N2O) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003-2019) of measurements at the Kellogg Biological Station Long-Term Ecological Research (LTER)/Long-Term Agroecosystem Research (LTAR) site to better understand the controls of N2O emissions in four corn-soybean-winter wheat rotations employing conventional, no-till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily N2O fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2-day precipitation, water-filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%-42% of daily N2O flux variability in the test data, with greater predictability for the corn phase in each system. The long-term rotations showed different controlling factors and threshold conditions influencing N2O emissions. In the conventional system, the model identified ammonium (>15 kg N ha-1) and daily air temperature (>23°C) as the most influential variables; in the no-till system, climate variables such as precipitation and air temperature were important variables. In low-input and organic systems, where red clover (Trifolium repens L.; before corn) and cereal rye (Secale cereale L.; before soybean) cover crops were integrated, nitrate was the predominant predictor of N2O emissions, followed by precipitation and air temperature. In low-input and biologically based systems, red clover residues increased soil nitrogen availability to influence N2O emissions. Long-term data facilitated machine learning for predicting N2O emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.

Adapting to changing methodology in a long‐term experiment

AbstractLong‐term experiments are critical for understanding ecological processes, but their management comes with unique challenges. As time passes, projects may encounter unavoidable changes due to external factors, like availability of materials, affecting aspects of their research methodology. At the Kellogg Biological Station Long‐Term Ecological Research Site, one of the many National Science Foundation‐funded long‐term research stations, a three‐decade project recently experienced a supply‐chain‐induced change in insect sampling methodology in their lady beetle observation study. Since 1989, lady beetles (Coleoptera: Coccinellidae) have been sampled weekly over the growing season using yellow sticky cards. In 2021, the original sticky traps were discontinued by the manufacturer and replaced with a similar, but not identical trap. We conducted a 3‐year study while the new traps were phased in to examine how the trap change would impact the observed biodiversity patterns at the site. We examined community metrics and individual taxa captures to examine within‐year and between‐year differences in performance between the card types. Overall, we noted several small but statistically detectable differences in capture patterns between the two trap types. After accounting for other sources of variation, we observed a difference in Shannon diversity of insects captured on the two card types, but not richness or abundance, for the overall insect community. Yet, these differences were dwarfed by the magnitude of difference observed between years within card types. For individual taxa, similar patterns held: between trap differences could be detected statistically, but the number of differences in capture rate between trap types was less than the number of differences observed for the same trap, between years. Thus, we conclude that while subtle changes in methodology could impact data produced in long‐term experiments; in this case, the magnitude of this change is smaller than other factors such as time and plant treatment. However, if sustained changes in the capture rates of focal taxa are observed, future data users may use our observations to specifically quantify and correct for these shifting patterns related to the protocol change.

Surface Resistance Controls Differences in Evapotranspiration Between Croplands and Prairies in U.S. Corn Belt Sites

AbstractWater returned to the atmosphere as evapotranspiration (ET) is approximately 1.6x global river discharge and has wide‐reaching impacts on groundwater and streamflow. In the U.S. Midwest, widespread land conversion from prairie to pasture to cropland has altered spatiotemporal patterns of ET, yet there is not consensus on the direction of change or the mechanisms controlling changes. We measured ET at three locations within the Long‐Term Agroecosystem Research network along a latitudinal gradient with paired rainfed cropland and prairie sites at each location. At the northern locations, the Upper Mississippi River Basin (UMRB) and Kellogg Biological Station (KBS), the cropland has annual ET that is 84 and 29 mm/year (22% and 5%) higher, respectively, caused primarily by higher ET during springtime when fields are fallow. At the southern location, the Central Mississippi River Basin (CMRB), the prairie has 69 mm/year (11%) higher ET, primarily due to a longer growing season. Differences in climate and that the CMRB prairie is remnant native prairie, while the UMRB and KBS prairies are restored, make it challenging to attribute differences to specific mechanisms. To accomplish this, we examine the energy balance using the Two‐Resistance Method (TRM). Results from the TRM demonstrate that higher surface conductance in croplands is the primary factor leading to higher springtime ET from croplands, relative to prairies. Results from this study provide insight into impacts of warm season grasses on the hydrology of the U.S. Corn Belt by providing a mechanistic understanding of how land use change affects the water budget.

Machine learning algorithms improve MODIS GPP estimates in United States croplands

Introduction: Machine learning methods combined with satellite imagery have the potential to improve estimates of carbon uptake of terrestrial ecosystems, including croplands. Studying carbon uptake patterns across the U.S. using research networks, like the Long-Term Agroecosystem Research (LTAR) network, can allow for the study of broader trends in crop productivity and sustainability.Methods: In this study, gross primary productivity (GPP) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) for three LTAR cropland sites were integrated for use in a machine learning modeling effort. They are Kellogg Biological Station (KBS, 2 towers and 20 site-years), Upper Mississippi River Basin (UMRB - Rosemount, 1 tower and 12 site-years), and Platte River High Plains Aquifer (PRHPA, 3 towers and 52 site-years). All sites were planted to maize (Zea mays L.) and soybean (Glycine max L.). The MODIS GPP product was initially compared to in-situ measurements from Eddy Covariance (EC) instruments at each site and then to all sites combined. Next, machine learning algorithms were used to create refined GPP estimates using air temperature, precipitation, crop type (maize or soybean), agroecosystem, and the MODIS GPP product as inputs. The AutoML program in the h2o package tested a variety of individual and combined algorithms, including Gradient Boosting Machines (GBM), eXtreme Gradient Boosting Models (XGBoost), and Stacked Ensemble.Results and discussion: The coefficient of determination (r2) of the raw comparison (MODIS GPP to EC GPP) was 0.38, prior to machine learning model incorporation. The optimal model for simulating GPP across all sites was a Stacked Ensemble type with a validated r2 value of 0.87, RMSE of 2.62 units, and MAE of 1.59. The machine learning methodology was able to successfully simulate GPP across three agroecosystems and two crops.

The effect of soil degradation on human, animal and plant health

The effect of soil degradation on human, animal and plant health Soil degradation is the biological, physical, chemical, and biological decline of soil quality or health. This phenomenon is problematic because it reduces the capacity of soils to support plant and animal life. Additionally, it reduces the ability of soils to regulate essential ecosystem services such as water regulation and nutrient cycling. Soil degradation has become a global issue and has come about largely due to land conversion from forests and grasslands to intensive agricultural practices. Land conversion depletes soil organic matter values, releasing large amounts of carbon dioxide into the atmosphere, contributing to global climate change and reducing soil fertility and soil health. Dr. Christine Sprunger, soil scientist and ecologist at W.K. Kellogg Biological Station (KBS) and the Department of Plant, Soil, and Microbial Sciences at Michigan State University, discusses the danger of soil degradation on human and animal life, and what we can do to mitigate it.

Management history and mixture evenness affect the ecosystem services from a crimson clover-rye cover crop

Cover crop mixtures of functionally distinct species may increase multiple ecosystem services, depending on their productivity and composition. We assessed how unique management legacies, and resulting soil fertility properties, influenced the functions provided by a cover crop mixture. We used the Kellogg Biological Station’s long-term Main Cropping Systems Experiment to test the legacy effects of four management systems (ranging from conventional to organic) on the production and function of crimson clover and cereal rye cover crops grown alone and in mixture, with a focus on their potential for improving agroecosystem N retention and supply. We applied bromide as a conservative tracer of solute flow in the soil profile to assess management legacy and cover crop treatment effects on potential anion leaching. A history of ecological nutrient management practices, including long-term cover crop use, increased soil fertility. Higher soil fertility, in turn, increased spring cover crop biomass and mixture evenness, and percent recovery of bromide, an estimate of soil anion retention. Mixture evenness was positively related to higher cover crop C/N ratios, N retention in corn biomass, and percent of clover N from fixation. In legacies with lower soil fertility, clover was more competitive than rye in the mixtures. Our results show that the benefits from cover crop mixtures depend on the management context into which they are adopted. In lower fertility soils with higher nutrient leaching potential, we suggest increasing crop functional diversity by including single species cover crops at different points within a crop rotation to build soil fertility. Once background soil fertility has been developed, farmers could include mixtures in their agroecosystems to maximize the functions provided by cover crops.

Sensitive Measures of Soil Health Reveal Carbon Stability Across a Management Intensity and Plant Biodiversity Gradient

Soil carbon (C) is a major driver of soil health, yet little is known regarding how sensitive measures of soil C shift temporally within a single growing season in response to short-term weather perturbations. Our study aimed to i) Examine how long-term management impacts soil C cycling and stability across a management intensity and plant biodiversity gradient and ii) Assess how sensitive soil health indicators change temporally over the course of a single growing season in response to recent weather patterns. Here we quantify a variety of sensitive soil C measures at four time points across the 2021 growing season at the W.K. Kellogg Biological Station’s Long Term Ecological Research Trial (LTER) located in southwest Michigan, USA. The eight systems sampled included four annual soybean (Glycine max) systems that ranged in management intensity (conventional, no-till, reduced input, and biologically-based), two perennial biofuel cropping systems (switchgrass (Panicum virgatum) and hybrid poplars (Populus nigra x P.maximowiczii)), and two unmanaged systems (early successional system and a mown but never tilled grassland). We found that unmanaged systems with increased perenniality enhanced mineralizable C (Min C) and permanganate oxidizable C (POXC) values. Additionally, all soil health indicators were found to be sensitive to changes in short-term weather perturbations over the course of the growing season. The implications of this study are threefold. First, this study assess indicators of labile and stable C pools over the course of the growing season and reflects the stability of soil C in different systems. Second, POXC, Min C, and ß-glucosidase (GLU) activity are sensitive soil health indicators that fluctuate temporally, which means that these soil health indicators could help elucidate the impact that weather patterns have on soil C dynamics. Lastly, for effective monitoring of soil C, sampling time and frequency should be considered for a comprehensive understanding of soil C cycling within a system.

Arbuscular mycorrhizal fungal community responses to drought and nitrogen fertilization in switchgrass stands

Anthropogenic global change is increasing the severity and frequency of abiotic stresses such as drought that are likely to affect soil communities. Arbuscular mycorrhizal fungi (AMF) play important roles in many soil processes, so it is important to understand how drought affects AMF biodiversity. This is especially relevant in agricultural systems where crops rely on AMF associations for water and nutrient uptake, and where management decisions such as crop selection and fertilizer application may influence how the AMF community responds to drought. In this study, we examined the effects of reduced precipitation and nitrogen fertilization on AMF richness, community composition, and root and soil colonization in monocultures of two cultivars of switchgrass (Panicum virgatum) grown for bioenergy feedstock. We conducted a two-year field experiment using rain-out shelters to manipulate precipitation in mature stands of switchgrass growing in a long-term nitrogen fertilization (0 or 56 kg N ha−1) experiment at the W.K. Kellogg Biological Station Long-Term Ecological Research site in Michigan, USA. We expected that AMF richness and colonization would decrease due to drought, as predicted by the stress exclusion hypothesis. Contrary to our expectations, we found that drought stress increased AMF species richness in fertilized plots by 15%; there was no effect of drought on AMF richness in unfertilized plots. Drought also significantly altered AMF community composition, primarily due to increases in Rhizophagus taxa abundance, and reduced AMF root colonization in switchgrass by 6%. We also found variation in AMF richness and colonization across switchgrass cultivars as well as sampling dates. The changes in AMF richness and composition that we observed in this study may have implications for perennial bioenergy feedstock selection and management as changes in AMF communities may feedback to affect host plants.

The broken window: An algorithm for quantifying and characterizing misleading trajectories in ecological processes

A core issue in temporal ecology is the concept of trajectory—that is, when can ecologists have reasonable assurance that they know where a system is going? In this paper, we describe a non-random resampling method to directly address the temporal aspects of scaling ecological observations by leveraging existing data. Findings from long-term research sites have been hugely influential in ecology because of their unprecedented longitudinal perspective, yet short-term studies more consistent with typical grant cycles and graduate programs are still the norm. We use long-term insights to create ‘broken windows,’ that is, reanalyze long-term studies from short-term observational perspectives to examine discontinuities in trends at differing temporal scales.The broken window algorithm connects our observations between the short-term and the long-term with an automated, systematic resampling approach: in short, we repeatedly ‘sample’ moving windows of data from existing long-term time series, and analyze these sampled data as if they represented the entire dataset. We then compile typical statistics used to describe the relationship in the sampled data, through repeated samplings, and then use these derived data to gain insights to the questions: 1) how often are the trends observed in short-term data misleading, and 2) can characteristics of these trends be used to predict our likelihood of being misled? We develop a systematic resampling approach, the ‘broken_window algorithm, and illustrate its utility with a case study of firefly observations produced at the Kellogg Biological Station Long-Term Ecological Research Site (KBS LTER). Through a variety of visualizations, summary statistics, and downstream analyses, we provide a standardized approach to evaluating the trajectory of a system, the amount of observation required to find a meaningful trajectory in similar systems, and a means of evaluating our confidence in our conclusions.

Cascading effects: insights from the U.S. Long Term Ecological Research Network

AbstractEcosystems across the United States are changing in complex and unpredictable ways and analysis of these changes requires coordinated, long‐term research. This paper is a product of a synthesis effort of the U.S. National Science Foundation funded Long‐Term Ecological Research (LTER) network addressing the LTER core research area of “populations and communities.” This analysis revealed that each LTER site had at least one compelling “story” about what their site would look like in 50–100 yr. As the stories were prepared, themes emerged, and the stories were group into papers along five themes: state change, connectivity, resilience, time lags, and cascading effects. This paper addresses the cascading effects theme and includes stories from the Bonanza Creek (boreal), Kellogg Biological Station (agricultural and freshwater), Palmer (Antarctica), and Harvard Forest (temperate forest) LTER sites. We define cascading effects very broadly to include a wide array of unforeseen chains of events that result from a variety of actions or changes in a system. While climate change is having important direct effects on boreal forests, indirect effects mediated by fire activity—severity, size, and return interval—have large cascading effects over the long term. In northeastern temperate forests, legacies of human management and disturbance affect the composition of current forests, which creates a cascade of effects that interact with the climate‐facilitated invasion of an exotic pest. In Antarctica, declining sea ice creates a cascade of effects including declines in Adèlie and increases in Gentoo penguins, changes in phytoplankton, and consequent changes in zooplankton populations. An invasion of an exotic species of lady beetle is likely to have important future effects on pest control and conservation of native species in agricultural landscapes. New studies of zebra mussels, a well‐studied invader, have established links between climate, the heat tolerance of the mussels, and harmful algal blooms. Collectively, these stories highlight the need for long‐term studies to sort out the complexities of different types of ecological cascades. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about trophic interactions as an important driver of ecosystem structure, function, services, and futures.

Effects of cover crops on soil CO2 and N2O emissions across topographically diverse agricultural landscapes in corn-soybean-wheat organic transition

Agriculture represents about 10 % of total greenhouse gases (GHG) emission in the US, in which carbon dioxide (CO2) and nitrous oxide (N2O) can greatly contribute to global warming and climate change. Organic farming has been increasingly popular as it provides long-term environmental benefits including GHG mitigation. Judicious use of cover crops in crop rotation systems has been considered an effective approach to sustain crop productivity during organic transition period. Here, we examined effects of cover crops on soil CO2 and N2O fluxes after the first year of organic transition in corn-soybean-wheat rotation across three contrasting topographies, particularly depression, slope, and summit at Kellogg Biological Station located in Southwest Michigan, USA. Three cover cropping systems used in this study were (1) cereal rye; (2) a mixture of cold susceptible species, namely, oat, winter pea, and radish, frost-killed in winter (WK); and (3) a winter hardy mixture of cold tolerant species, namely annual ryegrass, Dwarf Essex rapeseed, and crimson clover (WH). We also examined the effect of the size of incorporated plant residue fragments on soil CO2 and N2O emissions. Soil GHG emissions were the lowest on slopes as compared to depressions and summits. Cover crop mixtures including WH and WK increased soil CO2 fluxes emitted from the soil while cereal rye decreased it. Cutting plant residues prior to incorporation tended to decrease CO2 emission, suggesting that reducing fragment size of incorporated residue has a potential to mitigate GHG emissions from agricultural soils. In contrast, differences in N2O emissions for any of the studied effects were not statistically significant, likely due to very high variability of N2O fluxes. Soil temperature, moisture and total aboveground biomass were strongly associated with soil CO2 and N2O fluxes from soil.

Evaluating the applicability of soil moisture-based metrics for gauging the resiliency of rainfed agricultural systems in the midwestern United States

Measuring and improving resiliency is the key goal of climate risk management in rainfed agriculture. Currently, available metrics are generally used to qualitatively measure the resilience of agricultural systems at broader scales. Moreover, these metrics showed non-linear responses to climate variability, thus often fail to capture the temporal dynamics of resilience at field scales. Our objective for this study is to combine a few soil moisture-based metrics to gauge the resiliency of three promising rainfed agricultural treatments, namely the no-till, organic, and reduced input treatments against the conventional treatment. These treatments have been established at the Kellogg Biological Station Long-Term Ecological Research (KBS-LTER) experiment in a randomized complete block design with six blocks per treatment. All four of these treatments consisted of maize (Zea mays)-soybean (Glycine max)-winter wheat (Triticum aestivum) in rotation. Long-term (1993–2018) soil moisture data from this experiment was collected to compute the soil moisture metrics while the total crop biomass, crop yield, and soil organic carbon data were statistically analyzed to evaluate the robustness of the metrics to gauge the resiliency of these systems. Results have shown that, among the soil moisture metrics, the mean relative difference, the Spearman’s rank correlation coefficient, and soil water deficit index were suitable, while the index of temporal stability was not suitable to gauge the resiliency of different rainfed agricultural systems. The no-till treatment was identified as the most resilient treatment in terms of soil moisture retention, effectiveness for drought mitigation, and crop yields. Meanwhile, the reduced input treatment was the least resilient in terms of soil water conservation and drought recovery. The results of this study can be extended to other Midwest regions of the United States and similar climatological areas around the world.

Patient propagules: Do soil archives preserve the legacy of fungal and prokaryotic communities?

Soil archives are an important resource in agronomic and ecosystem sciences. If microbial communities could be reconstructed from archived soil DNA, as prehistoric plant communities are reconstructed via pollen data, soil archive resources would assume even greater value for reconstructing land-use history, forensic science, and biosphere modelling. Yet, the effects of long-term soil archival on the preservation of microbial DNA is still largely unknown. To address this, we assessed the capacity of high-throughput sequencing (Illumina MiSeq) of ITS (internal transcribed spacer) and prokaryotic 16S rRNA genes for reconstructing soil microbial communities across a 20 years time-series. We studied air-dried soil archives and fresh soil samples taken from Populus bioenergy and deciduous forest research plots at the Kellogg Biological Station. Habitat and archival time explained significant amounts of variation in soil microbial α- and β-diversity both in fungal and prokaryotic communities. We found that microbial richness, diversity, and abundance generally decreased with storage time, but varied between habitat and taxonomic groups. The high relative abundance of ectomycorrhizal species including Hebeloma and Cortinarius detected in older soil archives raises questions regarding traits such as long-term persistence and viability of ectomycorrhizal propagules in soils, with relevance to forest health and ecosystem succession. Talaromyces, Paecilomyces and Epicoccum spp. were detected in fresh and across 20-year-old archived soils and were also cultured from these soils demonstrating their long-term spore viability. In summary, we found that microbial DNA in air-dried soils archived over the past 20 years degraded with time, in a manner that differed between soil types and phylogenetic groups of microbes.

Predicting productivity: A trait-based analysis of variability in biomass yield among switchgrass feedstock cultivars

Plant trait variation in bioenergy crops is hypothesized to affect crop biomass production. The extent of this trait variation and its relationship to production should be explored to inform cultivar selection in different environments and with environmental change. Switchgrass (Panicum virgatum L.) is an important prospective bioenergy crop in North America because it has the potential for high productivity, a wide geographic range, and distinct ecotypes that differ in a number of traits likely to be associated with production. Commercial cultivars of switchgrass have been developed for specific traits, but few studies have assessed if variation in multiple distinct traits relates to productivity and if this relationship differs with environmental conditions. To determine how trait variation affects biomass production in switchgrass we sampled a field trial of 12 switchgrass cultivars of two ecotypes (eight upland, four lowland) being grown in a common garden at the W.K. Kellogg Biological Station, Michigan State University, Michigan, USA. Yield was measured annually for eight years (2010–2017). Between 2014 and 2016, we measured physiological, morphological, and system-level (weed suppression and recovery from drought) traits of these cultivars in the common garden. We used principal components analysis to summarize these traits into composite trait variables and developed linear models to assess the relationship between composite traits and yield across and within years. The 12 cultivars exhibited considerable trait variation that did not align entirely with ecotype. Composite trait axes representing height, phenology, and leaf nitrogen content were strong predictors of yield during stand establishment (years one to three) and in drought years, but were less predictive after stand maturation. Variation in yield across the eight years of the study was better explained by traits than growing season precipitation or drought severity. Interestingly, despite differences in traits, overall ecotype was not a strong predictor of interannual variation in yield especially during stand establishment. The results suggest that traits should be incorporated into agronomic decisions for planting and managing switchgrass cultivars in bioenergy production.

Drought minimized nitrogen fertilization effects on bioenergy feedstock quality

Switchgrass (Panicum virgatum) is one of the leading candidates for sustainable lignocellulosic biofuel production in North America. Most current management recommendations for switchgrass include applications of synthetic nitrogen (N) fertilizers to increase production, particularly when grown on marginal lands. However, this management can be costly to growers and have negative impacts on ecosystem functioning. Also, N-fertilization does not always result in higher yield in switchgrass and may have unintended effects on plant biomass quality, including cell wall composition, that can affect the efficiency of fermentation processes for biofuel production. Drought stress may reduce biomass responses to N-fertilization, further reducing the value of fertilizer application. To examine whether N-fertilization and reduced precipitation affected switchgrass productivity and cell wall composition, we conducted a two-year field experiment in mature stands of two switchgrass cultivars grown for bioenergy at the W.K. Kellogg Biological Station Long Term Ecological Research Site in Michigan, USA. Nitrogen was added at a rate of 56 kg N ha−1 (urea and ammonium nitrate), and precipitation was reduced using rainout shelters. Overall, we did not observe any effect of N-fertilization on biomass production. However, under ambient rainfall conditions, N-fertilization altered switchgrass biomass quality by reducing hemicellulose. Reduced precipitation minimized the effects of N-fertilization on switchgrass cell wall composition. Switchgrass is a relatively drought-tolerant species, and our results indicate that this crop will be a viable bioenergy feedstock even in a changing climate. However, in this study, N-fertilization had no effect on biomass quality or quantity under drought conditions.

In Situ Quantification of Biological N2 Production Using Naturally Occurring 15N15N.

We describe an approach for determining biological N2 production in soils based on the proportions of naturally occurring 15N15N in N2. Laboratory incubation experiments reveal that biological N2 production, whether by denitrification or anaerobic ammonia oxidation, yields proportions of 15N15N in N2 that are within 1‰ of that predicted for a random distribution of 15N and 14N atoms. This relatively invariant isotopic signature contrasts with that of the atmosphere, which has 15N15N proportions in excess of the random distribution by 19.1 ± 0.1‰. Depth profiles of gases in agricultural soils from the Kellogg Biological Station Long-Term Ecological Research site show biological N2 accumulation that accounts for up to 1.6% of the soil N2. One-dimensional reaction-diffusion modeling of these soil profiles suggests that subsurface N2 pulses leading to surface emission rates as low as 0.3 mmol N2 m–2 d–1 can be detected with current analytical precision, decoupled from N2O production.

Spatially tracking carbon through the root–rhizosphere–soil system using laser ablation‐IRMS

AbstractThe intimate relationships between plant roots, rhizosphere, and soil are fostered by the release of organic compounds from the plant into soil through various forms of rhizodeposition and the simultaneous harvesting of nutrients from the soil to the plant. Here we present a method to spatially track and map the migration of plant‐derived carbon (C) through roots into the rhizosphere and surrounding soil using laser ablation‐isotope ratio mass spectrometry (LA‐IRMS). We used switchgrass microcosms containing soil from field plots at the Kellogg Biological Station (Hickory Corners, Michigan, USA) which have been cropped with switchgrass since 2008. We used a 13CO2 tracer to isotopically label switchgrass plants for two diel cycles and tracked subsequent movement of labeled C using the spatially specific (< 100 µm resolution) δ13C analysis enabled by LA‐IRMS. This approach permitted assessment of variable C flow through different roots and enabled mapping of spatial variability of C allocation to the rhizosphere. Highly 13C‐enriched C (consistent with production during the 13CO2 application period) extended ≈ 0.5–1 mm from the root into the soil, suggesting that the majority of recent plant‐derived C was within this distance of the root after 48 h. Tracking the physical extent of root exudation into the rhizosphere can help evaluate the localization of plant‐microbe interactions in highly variable subsurface environments, and the use of the isotopic label can differentiate freshly fixed C (presumably from root exudates) from other types of subsurface C (e.g., plant necromass and microbial turnover). The LA‐IRMS technique may also serve as a valuable screening technique to identify areas of high activity for additional microbial or geochemical assays.

High-resolution elemental mapping of the root-rhizosphere-soil continuum using laser-induced breakdown spectroscopy (LIBS)

Understanding the complex chemical nature of root-soil interactions is essential for building the next generation of sustainable agricultural systems and facilitating long-term environmental remediation strategies. Techniques currently suited to investigate spatial controls on nutrient exchange in plant rhizospheres, however, are hindered by limitations in throughput, cost, analytical scope, and sample preparation needs. We describe here a method for rapid, high-resolution (∼100 μm), multi-element imaging of both organic content and inorganic constituents in root-rhizosphere-soil systems using laser-induced breakdown spectroscopy (LIBS). A switchgrass assemblage (Panicum virgatum) was grown in compact rhizotrons containing a sandy loam Alfisol from the Kellogg Biological Station (KBS), Michigan, USA. Root-soil samples were extracted using custom plastic coring devices for live root sampling and a modified drill press for sectioning frozen stabilized soil. A 266 nm, Nd:YAG laser was rastered over ∼10 mm2 sample surfaces and broadband spectra from single-pulse ablations were collected and mapped to discrete XY spatial coordinates for simultaneous imaging of 17 macronutrients, micronutrients and matrix elements. In order to rapidly process LIBS raster data and investigate chemical trends in the rhizosphere, an open-source Python module was developed, in which we used a novel calibration-free masking algorithm (based on principal components analysis (PCA) of normalized spectral intensities) to discriminate soil mineral grains, root fragments, and associated rhizosphere regions. We observed fine-scale chemical gradients within only a millimeter of switchgrass roots, consistent with rhizodeposition of organic compounds and proximal uptake of inorganic nutrients. Detection of trace element and carbon accumulations with diagnostic spectral signatures in the rhizosphere suggested the presence of residues (detritusphere) that could serve as sites of preferential microbial accumulation. These results highlight potential applications of LIBS within the growing field of spatially-resolved plant-soil analysis and extend its versatile imaging capabilities to the complex root-rhizosphere-soil network.

Structural Stability Conditions Soil Carbon Gains from Compost Management and Rotational Diversity

Core Ideas Soil structure and carbon pools were quantified in a 20‐year field crop trial. Compost management influenced soil aggregate size, labile carbon, and total carbon. An increased rotational diversity from one to five crops enhanced soil aggregate stability. Use of moderate compost increased soil carbon by 20% relative to fertilizer. Understanding processes that underlie soil carbon gains and enhance structural stability is key to sustainable production of intensively managed row crops. Long term consequences of crop diversity and interactions with compost management have rarely been tested with economically feasible, moderate rates of organic amendments. We investigated soil aggregation and carbon pools over twenty years on a field crop experiment that allowed quantification of the impact of rotational diversity within two integrated management regimes, one based on compost (3 Mg ha−1) and the other inorganic fertilizer, both at about 100 kg N ha−1 annually. The Living Field Laboratory (LFL) experimental study where the study was conducted is located at the W.K. Kellogg Biological Station in southwest Michigan. The rotational diversity treatments investigated included continuous corn (Zea mays L.) (CC), corn–soybean [Glycine max (L.) Merr.] rotation (CS), corn–soybean–wheat (Triticum aestivum L.) rotation (CSW), and corn–soybean–wheat rotation with a cover crop (CSWco). Soil C status in 1993 was 2584 g m−2 and by 2013 increased in integrated fertilizer and compost plots to 3026 and 3672 g kg−1, respectively. Compost management enhanced soil labile C, organic C and the proportion of large macroaggregates (>2000 μm size fraction). Rotational diversity did not influence total soil C but was positively associated with soil aggregate stability and aggregate carbon pool size. The study findings were consistent with integrated organic and inorganic nutrient management as interacting positively with crop diversity, to support gains in soil structural stability and C accrual.