Cover crop application on dredged sediments increases corn yield through microorganism-associated enzyme-driven nutrient mineralization.

doi:10.21203/rs.3.rs-2874402/v1

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Research Article

Cover crop application on dredged sediments increases corn yield through microorganism-associated enzyme-driven nutrient mineralization.

https://doi.org/10.21203/rs.3.rs-2874402/v1

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Background & Aims

Common strategies to mitigate soil degradation of agricultural soils include cover crop application and soil amendment addition. Applying dredged sediments as a soil amendment is gaining popularity since they often provide benefits other amendments lack; however, their use with cover crops is largely unexplored. To understand how cover crop use changes the restorative properties of dredged sediments, we assessed soil physical and chemical properties, enzymatic activities, and corn yield for plots of dredged sediments with and without a cover crop.

Methods

We assessed soil texture, bulk density, water content, pH, nitrogen, phosphorus, cation exchange capacity, calcium, magnesium, potassium and organic matter content, as well as alkaline phosphatase, β-glucosidase, leucine aminopeptidase, peroxidase, polyphenol oxidase and urease enzyme activities and crop responses and yields in manipulated dredged sediments with and without a cover crop over three collection periods: pre-cover crop, post-cover crop, and post-harvest.

Results

Cover crop application on dredged sediments increased corn yields by ~ 24% when compared to dredged sediments alone. Increases in corn yield were driven by changes in nutrient mineralization, specifically within the nitrogen cycle. The physical and chemical properties of dredged sediments remained unchanged regardless of cover crop application.

Conclusion

Our results suggest that when cover crops are applied to dredged sediments, crop yield increased through microorganism-driven nutrient mineralization. However, the physical and chemical environment remained optimal for corn growth within dredged sediments, regardless of cover crop application. This research is a vital step into understanding the use of dredged sediments in agricultural soil systems.

soil amendment

Zea mays

agriculture

winter rye

soil enzyme activity

Secale cereale

Row cropping agricultural practices decimate soil systems, contributing to the annual degradation of seventy-five billion tons of productive agricultural soils worldwide (Wallander 2013; Baumhardt et al. 2015; Richardson and Dooley 2017; Scherr and Yadav 2020). Agricultural soils are deemed degraded when there is a breakdown between their physical structures, chemical environment, and/or the function and diversity of their biota (NCRS USDA 2020b). These soil properties are intrinsically linked; therefore, when one or more groups of soil properties degrade, this creates an imbalance within the soil environment which negatively impacts plant growth and agricultural production (Hellerstein et al. 2019). In the US, the loss of productive agricultural soils results in a 14% reduction in crop productions, leading to a roughly $2.8 billion decline in annual revenue (den Biggelaar et al. 2001; Eswaran et al. 2001; Thaler et al. 2021). Economic and environmental losses due to soil degradation establish the need for more beneficial agricultural practices to prevent soil loss and/or restore already degraded soils.

Agricultural soil degradation is a direct result of the continued use of intensive and inefficient agricultural practices such as monocropping, tilling, and synthetic fertilizer amendments that are traditionally used to mass-produce crops. Monocropping, or growing a single crop year after year, can reduce aggregate stability and deplete the soil of nutrients, leading to an inhospitable environment for microorganisms (Crookston et al. 1991; Nevens and Reheul 2001). Direct soil manipulation through tilling can alter the soil’s bulk density (BD) and compaction as well as reduce beneficial fungal communities (Lal et al. 1989; Al-Kaisi and Kwaw-Mensah 2007; Karlen et al. 2013). Traditional synthetic fertilizers supplement only specific nutrients and can also reduce the function of beneficial microorganisms, which often leads to inhospitable environments for crops over the long-term (Liebig et al. 2002; Russell et al. 2006; Hellerstein et al. 2019; Zhu et al. 2016; Srour et al. 2020).

To mitigate soil degradation, farmers have adopted field management practices including applying organic soil amendments and using cover crops (Wallander 2013; Hellerstein et al. 2019; NRCS USDA 2020a; APHIS USDA 2020). Organic soil amendments are additives derived from either animal waste or plants, such as manure, biosolids, or biochar. These additives are used to improve soil structure, increase available nutrients, and enhance the function of beneficial soil biota (US EPA 2013; ERS USDA 2019). The application of organic soil amendments can increase organic matter (OM) within the soils, leading to increased soil aggregate stability and carbon (C) sequestration (Whalen et al. 2003; Tian et al. 2015; Foster et al. 2016; Liu et al. 2020). This increase in soil stability leads to increases in soil cation exchange capacity (CEC) and nutrient availability, as most organic soil amendments, except for biochar, often contain high levels of nitrogen (N), phosphorus (P), potassium (K), and other micronutrients (Barbarick et al. 2012; Filiberto and Gaunt 2013; Hao et al. 2015). As a result, the influx of nutrients added by organic soil amendments can lead to a 2-5x increase in microbial-associated enzymatic activity (Fernández et al. 2009; Jin 2010; Song et al. 2019; Ozlu et al. 2019). Thus, soil property changes due to the application of organic soil amendments can increase crop production by up to 50% compared to unamended agricultural soils, especially for grasses and grains such as corn (Singer et al. 2004; Sigua 2005; Barbarick et al. 2012; Lehmann et al. 2011; Hao et al. 2015; Hellerstein et al. 2019).

A less established organic soil amendment that can benefit degraded agricultural soils is dredged sediments. Dredged sediments are the mechanically removed build-up of sediments in waterways that can have high levels of macro and micronutrients and increased water holding capabilities (Averett et al. 1990). The application of dredged sediments can improve degraded soil physical structures such as BD, texture, and water retention (Canet et al. 2003; Sigua 2005; Darmody and Diaz 2017). When applied to agricultural soils, dredged sediments can also increase available N, P, K, and micronutrient content (Fonseca et al. 1998; Sigua 2005) and neutralize soil pH, depending on their origin source (Sigua 2005; Daniels et al. 2007; Baniulyte et al. 2009; Darmody and Diaz 2017). Dredged sediments also contain beneficial microorganisms which can increase crop yields through increased nutrient cycling (Rúa et al. 2023). Changes in soil properties due to dredged sediments application can lead to increased yield and production for a variety of crops, including corn, soybeans, tomatoes, and lettuce (Sigua 2005; Koropchak et al. 2016; Canet et al. 2003; Daniels et al. 2007; Darmody and Diaz 2017).

Another way to mitigate soil degradation from row cropping agriculture is by using a cover crop during the non-growing seasons. Planting cover crops during non-growing seasons can enhance soil structure by reducing the loss of both soil aggregates and moisture. These enhancements help maintain soil’s air and water ratios while increasing BD (Gabriel and Quemada 2011; Steele et al. 2012). Using cover crops also increases OM within the soil, which often leads to improved function of the beneficial soil microbiota (Kim et al. 2020). Additionally, the use of cover crops can improve environmental quality by reducing run-off and nutrient leaching, which retains needed nutrients through the non-growing season (Gabriel and Quemada 2011; Steele et al. 2012).

The use of a cover crop in combination with established organic soil amendments can further improve soil physical and chemical properties as well as the enzymatic activity in agricultural soils, leading to overall greater crop production (Fernandez et al. 2016; Sánchez de Cima et al. 2016; Ashworth et al. 2017; Adeli et al. 2019; Raut et al. 2020). However, interactions between cover crops and dredged sediments are still largely unknown. Here, we investigated the application of a cover crop on dredged sediments, assessing changes in soil properties and corn responses. Following the USDA guidelines for assessing soil properties (NRCS USDA 2020b), we quantified soil texture, BD, and water content (physical properties), nutrient content, CEC, and pH (chemical properties), the activities of six microorganism-associated extracellular enzymes (biological properties), and the growth and reproduction of corn (yield). We hypothesized that adding a cover crop would improve the physical and chemical properties and increase enzymatic activities of dredged sediments compared to dredged sediments left fallow over the winter. We also hypothesized that corn production would be greater after applying a cover crop than corn grown on dredged sediments left fallow over winter. Finally, we hypothesized that changes to the physical properties, chemical properties, and enzymatic activities of dredged sediments due to the application of the cover crop would drive associated changes in corn production. Our results highlight the importance of assessing dredged sediments as an organic amendment intended for use within agricultural systems.

Site Description

To examine the potential for a cover crop to improve soil health and plant production on dredged sediments, we manipulated the presence of a cover crop in plots at the Great Lakes Dredged Material Center for Innovation (GLDMCI) located in Toledo, Ohio (41.6700354^oN, -83.5029711^oW; Rúa et al. 2023). The GLDMCI was created between 2016 and 2017 by pumping ~ 8,800 m³ of dredged sediments from the Toledo Harbor and the Maumee River into four ~ 1 ha plots (Hull and Associates 2018). Dredged sediments in each plot were allowed to dry and acclimate to the on-land environment for two years prior to the experiment (Rúa et al. 2023).

Plot Preparation and Planting

In November 2018, the two available 1 ha plots were mowed and disked to remove existing vegetation. Then one plot was broadcast seeded with the common Midwestern cover crop winter rye (Secale cereale; The CISCO Companies, Indianapolis, IN, USA; ‘cover crop’) while the second plot was left fallow (‘control’). Approximately 43 days following the last freeze (CFAES 2022), both plots were tilled such that the existing vegetation was sowed into the dredged material prior to sowing rows of corn (variety: W2903DP; Wellman Seeds Inc., Delphos, OH, USA) via tractor on 15 May 2019. To eliminate weedy vegetation, halfway through the growing season (8 July 2019), both plots were sprayed with the herbicide Glyphosate with a 53.8% concentration at a rate of 0.0004 L/m² (1.5 qt per acre; Buccaneer 5; CommoditAg, Effingham, IL, USA). To mirror local farming practices, plots were also fertilized with 48% ammonium sulfate (AMS; CommoditAg, Effingham, IL, USA) on 8 July 2019 at a rate of 0.0002 kg/m² into 75.7 L of water per 0.4057 ha (1.5 lbs into 20 gallons of water per acre).

Soil Sample Collection

To assess differences in soil properties due to the use of a cover crop, we collected soil samples for analyses of physical, biological, and chemical properties from 10 sampling locations per plot at three collection times throughout the experiment: following site prep but prior to cover crop planting (12 November 2018, ‘pre-cover crop’), after completion of the cover crop life cycle / prior to corn planting (12 April 2019; ‘post-cover crop’) and following final corn harvest (12 October 2019; ‘post-harvest’). Soil samples from 10 haphazardly chosen locations were collected per plot (20 samples total), and each location was marked for future sampling. Samples were transported within six hours of collection to the Rúa lab at Wright State University in Dayton, OH, for further analyses.

Upon arriving at the Rúa lab, a bag containing ~ 750 g of soil from each sampling location (n = 20) was stored at -20 ^oC for up to three months prior to analysis for physical and chemical properties. In addition, one 2 mL tube per sampling site (n = 20) was stored on dry ice during transport and then stored at -20 ^oC for up to six months until analyzed for enzymatic activities. Finally, for post-harvest sampling only (12 October 2019), we also collected one soil core (11.5 cm depth x 10.5 cm diameter) from each sampling location and stored the core at room temperature for 18 hours until processing for bulk density measurement.

Soil Physical and Chemical Properties

The soil physical properties, including soil texture, BD, and gravimetric moisture content, were quantified to assess the effects of cover crop application. To assess soil texture, we measured the % sand, % silt, and % clay within each soil sample using the LaMotte soil texture kit (LaMotte, Chestertown, MD, USA). Gravimetric water content (‘moisture’) was quantified using the mass ratio such that the samples were weighed wet, dried for 48 hours at 105 ^oC, reweighed dry, and then the ratio of dry to weight was taken to determine the percent of water in each sample (Woods et al. 2019).

To measure the BD of soils, we multiplied the ratio of the volume of the soil core (‘field moist volume’) to the weight of the soil in the core (‘field moist mass’) by the ratio of the subsample wet mass to the subsample dry mass from the moisture calculation to calculate the bulk density of the sample (Eq. 1; Onufrak et al. 2019). As soil cores were not used for the first and second collection times, we acquired field moist volume using a 250 mL beaker as the ‘core’, filled it with the soil sample, took the final weight (‘field moist mass’) and used these values to calculate the bulk density of the sample (Eq. 1). To determine any differences between the core and modified methods, we ran the modified method using the same soils from the core method for three samples per treatment of the third collection time and compared the values obtained per method. The side-by-side comparison resulted in a difference of 0.2652 g/mL between the methods that was then used as a correction factor for the first and second collection times.

$$g/{ml}^{-1}=\left(\frac{field moist mass \left(g\right)}{field moist volume \left(mL\right)}\right)*\left(\frac{subsample dry mass \left(g\right)}{subsample wet mass \left(g\right)}\right)$$

Equation 1. Soil bulk density calculation.

We measured several soil chemical properties, including total N, total P, K, magnesium (Mg⁺), calcium (Ca⁺), CEC, pH, and % OM content. Soil pH was assessed using a 1:2 soil to water ratio and was measured using a Fisherbrand™ accumet™ AB15 Basic and BioBasic™ pH / mV / ^oC meter (ThermoFisher, Waltham, MA, USA; Woods et al. 2019). Three composite samples from each sampling location per plot (Sample A = 1, 2, 3; Sample B = 4, 5, 6; Sample C = 7, 8, 9, 10) for each collection time (n = 9) were sent to A & L Great Lakes Laboratories (Fort Wayne, IN, USA) for CEC, K, Mg⁺, Ca⁺, and OM analysis following EPA recommended laboratory protocols. Total N and total P were measured by the Midden Lab at Bowling Green State University (Bowling Green, OH, USA) using alkaline persulfate digestion with automated colorimetric detection of N and P using an AQ2 + discrete chemical analyzer (Seal Analytical, Mequon, WI, USA), as recommended by the EPA and US Geological Survey (Francy et al. 2020).

Bulk Soil Enzyme Activity

We quantified activities of six extracellular enzymes, including the fluorometric enzymes of alkaline phosphatase (AKP), β-glucosidase (BG), and leucine aminopeptidase (LAP), as well as the colorimetric enzymes of peroxidase (PER), polyphenol oxidase (PPO), and urease (UR). All enzyme assays followed protocols modified from Woods et al. (2019) and Sinsabaugh et al. (2000) and used a homogeneous soil slurry with 0.25 g dry weight of soil sample and 31.5 mL of 50 mM sodium acetate buffer (pH 5.6), except for AKP which required 31.5 mL of NaOH Tris buffer (pH 11.0). Soil slurries (‘soil homogenates’) were stored in the dark at 4 ^oC for up to one week prior to analysis. All enzymes were assayed in triplicate alongside a buffer blank, substrate blank, soil homogenate blank, and soil homogenate and substrate mix using a 96-well plate. Fluorometric enzyme analyses also included a standard curve for each sample.

For AKP and BG assays, the fluorescence indicator Methylumbelliferone was used in the form of the substrates 4-Methylumbelliferyl phosphate and 4-Methylumbelliferyl beta-D-glycopyranoside. For LAP, assays were conducted with the substrate L-Leucine-7-amid-4-methylcoumarin hydrochloride and 7-Amino-4-methylcoumarin. Once all reagents and samples were added to the 96-well plate, the prepared plates were left in the dark to incubate at room temperature (22 ^oC) for the following times: AKP, nine hours; BG, one to eight hours; and LAP, 96 hours. Following incubation, a NaOH stop solution was added to all wells, and the samples were incubated at room temperature for 10 minutes before reading at 360 nm excitation and 450 nm emission on a BioTek Synergy HT microplate reader (Agilent, Santa Clara, CA, USA). Using the dilution factor (DF = mL volume of buffer / g of soil sample), extinction coefficient $(\text{Ɛ}$; slope of the sample’s standard curves), and the calculated net fluorescence units (NFU; Eq. 2), all fluorometric enzyme activities were expressed as µmol h^− 1 g^− 1 using Eq. 3.

$$NFU =SH -SHB-B*\left(\frac{slope of standard with sample}{slope of standard with buffer}\right)$$

Equation 2. Net Fluorescence Unit (NFU) calculation for fluorometric enzymatic activities where ‘SH’ is the soil homogenate + substrate fluorescence, ‘SHB’ is the soil homogenate blank fluorescence, and ‘B’ is the substrate blank fluorescence.

$${\mu }\text{m}\text{o}\text{l} {h}^{-1}{g}^{-1} =\frac{\left(\frac{\text{N}\text{F}\text{U}}{\frac{\text{Ɛ}}{0.25}\text{m}\text{L}}\right)}{\begin{array}{c}\left(incubation time*\left(\frac{1}{DF}\right)*0.200 mL\right)\end{array}}$$

Equation 3. Fluorometric enzymatic activity calculation using the Net Fluorescence Unit (NFU) and Dilution Factor (DF) for alkaline phosphatase, β-glucosidase, and leucine aminopeptidase.

The colorimetric enzymes PER and PPO were tested using 25 µM 3,4-Dihydroxyphenylalanine with the addition of 3% hydrogen peroxide for PER. Plates were incubated in the dark for 24 hours at room temperature (22 ^oC). The plates were read at 450 nm emission on a Molecular Devices Corporation SpectraMax 190 microplate reader (Molecular Devices Corporation, Sunnyvale, CA, USA), and activity in µmol h^− 1 g^− 1 was calculated using net absorbance units (NAU = soil homogenate absorbance – soil homogenate blank absorbance – substrate blank absorbance) and the extinction coefficient $\left(\text{Ɛ}\right)$ 1.8446 for PER and 2.4942 for PPO (Eq. 4).

$${\mu }\text{m}\text{o}\text{l} {h}^{-1}{g}^{-1} =\frac{\text{N}\text{A}\text{U} *Buffer volume \left(ml\right)}{\left(\text{Ɛ}\text{*}0.200 \text{m}\text{L}*Incubation Time \right(hr\left)*sample \right(g)}$$

Equation 4. Colorimetric enzyme activity calculation using the Net Absorbance Unit calculation (NAU) for peroxidase, polyphenol oxidase, and urease.

UR activity was tested using 10 µl of a 400 mM Urea substrate with an incubation time of between two and seven hours at room temperature (22 ^oC) in the dark before adding 40 µl of both salicylate and cyanurate acid and a one-hour incubation time after the addition. We read the plates at 610 nm on a Molecular Devices Corporation SpectraMax 190 microplate reader (Molecular Devices Corporation, Sunnyvale, CA, USA). We used net absorbance units (NAU = soil homogenate absorbance – soil homogenate blank absorbance – substrate blank absorbance) and the extinction coefficient $\left(\text{Ɛ}\right)$ of 0.2403 to calculate UR activity in µmol NH₄ g^− 1 h^− 1 (Eq. 4).

Plant Responses

We assessed the response of corn to the cover crop treatment by measuring plant height, leaf count, reproduction status, photosynthetic efficiency, total (above and below ground) biomass, and final yield for 10 focal plants located at the previously sampled soil locations in each plot (total of 20 plants). We measured leaf count and plant height weekly throughout the growing season, starting from plant emergence on 24 May 2019 and ending with the final harvest on 12 October 2019. Plant height (cm) was assessed using a tape measure from the ground to the top of the arch of the highest fully formed leaf (APHIS USDA 2020). Leaf count was measured following the guidelines from the USDA (2020) by counting only the fully formed leaves with an arch. Leaf count was also used to indicate vegetative growth stages (i.e., for V6, the corn plant had six fully developed leaves). Reproductive growth stages of tassel (VT) and kernel development (R1-R6) were assigned when applicable (Nafziger 2017).

Photosynthetic efficiency, which evaluates the efficiency of a plant’s photosystem II to capture sunlight and transfer energy, was measured twice during the growing season, once during the vegetation stage (21 June 2019, day 63 of growth) and again during kernel production (31 August 2019, day 110 of growth). We measured photosynthetic efficiency (F_VM) using an OS-30 chlorophyll fluorometer (Opti-Sciences, Hudson, NH, USA) following the procedure outlined in Friedman et al. (2020). Briefly, we placed a closed clip on two different leaves, facing the top of the leaf, and allowed the clipped area to equilibrate for 30 minutes prior to light exposure. We then exposed the leaf enclosed by the clip to a low light emission followed by a high light emission and recorded the F_VM for each clip per plant. Photosynthetic efficiency was then calculated by averaging the F_VM of both clipped leaves for each plant.

To assess yield, we quantified number of ears, kernel count per ear and per plant, the weight of kernels per ear (g/ear) and per plant (g/plant), and total plant biomass (g). Above and below ground biomass were measured twice during the experiment, once to mimic silage harvest (31 August 2019) and once at final harvest (12 October 2019). For silage harvest, plants of similar height and leaf count located near the focal plants were used, while the final harvest plants were the focal plants themselves. For both harvest collections, plants were hand harvested, and the roots were disconnected from the stalk and cleaned to remove excess soil. All plant material was then dried for 48 hours at 60 ^oC, and the above and below ground plant material was weighed separately (g). Ears were counted on each corn plant at final harvest. Next, kernels were hand removed from the cob, counted for each ear, and totaled for each plant. Finally, kernels were weighed (g) per ear and per plant.

Statistical Analysis

To determine if the application of a cover crop improved dredged sediment soil property and corn responses compared to a plot left fallow, we ran univariate and multivariate statistical analyses. All statistical analyses were performed in the statistical programming environment R, version 4.2.1 (R Core Team 2022). Data were normalized through log transformation as needed to match model assumptions. All data visualizations were created using the ggplot2 (Wickham et al. 2022) and ggbiplot (Vu 2011) packages.

We created separate linear models for each soil property metric to quantify changes in soil physical properties, chemical properties, and enzymatic activities due to adding a cover crop compared to a plot left fallow. We created linear models using the lm() function in base R for % OM, CEC, Ca⁺, K, and Mg⁺ since these properties were assessed using only three composite samples per plot per collection period. We created linear mixed effects models using the lme() function in the nlme package (Pinheiro et al. 2021) for % sand, % silt, % clay, and pH and the log transformed data for BD, soil moisture, and activities for AKP, BG, LAP, PER, PPO, and UR with a random effect in each model for unique plant ID. All soil property models were created such that each soil property metric was a function of the interaction of treatment (cover crop vs. control) and collection time [pre-cover crop (12 November 2018), post-cover crop (12 April 2019), post-harvest (12 October 2019)]. We then tested each model with an analysis of variance (ANOVA) using the anova() function in base R. In cases with significant relationships between response variables and interaction terms, we performed post-hoc analyses using the emmeans() function in the emmeans package (Lenth et al. 2022), where all pair-wise comparisons a Tukey’s p-value adjustment method. To determine if soil property responses differed between groupings of treatments (cover crop vs. control), we used separate principal component analyses (PCAs) for physical, chemical, and enzyme properties were also created with the prcomp() function in base R to condense changes in properties into two linear principal components. We performed an analysis of similarity (ANOSIM) for all PCAs using the adonis2() function in vegan package (Oksanen et al. 2022).

To quantify changes in corn vegetative development between plots with or without a cover crop application, we created separate linear models for plant stage, height, leaf count, relative growth rate, and root to shoot ratio. We evaluated differences in development time by week due to cover crop use by creating a linear model using the lm() function in base R, using week of plant data collection as a function of the interaction between treatment (cover crop vs. control) and plant stage (VE - R6).

To assess differences in plant height over time, we created a linear model using the lme() function in the nlme package (Pinheiro et al. 2021), where height was a function of the interaction of treatment (cover crop vs. control) and week of data collection with unique plant IDs was a random effect. We then tested our model with repeated measures ANOVA using the anova() function in base R, with post-hoc analyses using the emmeans() function in the emmeans package (Lenth et al. 2022) where all pair-wise comparisons used a Tukey’s p-value adjustment method. We created a generalized linear mixed effects model with a Poisson distribution using the glmer() function in the lme4 package (Bates et al. 2022) to evaluate leaf count as a function of the interaction between treatment (cover crop vs. control) and week, with individual plant ID as the random effect. We tested our model with an ANOVA using the Anova() function in the car package (Fox et al. 2022) and post-hoc analyses for significant interactions using the emmeans() function in the emmeans package (Lenth et al. 2022) where all pair-wise comparisons used a Tukey’s p-value adjustment method.

To quantify changes in plant matter allocation, we calculated the ratio of roots (below ground biomass) to shoots (above ground biomass). We then created a linear model using the lm() function in base R, where the root to shoot ratio was a function of treatment (cover crop vs. control) and was evaluated for both silage and final harvests. We then tested our models with an ANOVA using the anova() function in base R. Significant interactions were subjected to post-hoc analyses using the emmeans() function in the emmeans package (Lenth et al. 2022) where all pair-wise comparisons used a Tukey’s p-value adjustment method.

Finally, we calculated the relative growth rate (RGR) by week for each plant by taking the height of the current week, subtracting it from the height in the previous week, and dividing that value by the height in the previous week (Eq. 5). We then evaluated changes in weekly RGR with a linear mixed effects model using the lme() function in the nlme package (Pinheiro et al. 2021), where RGR was a function of the interaction between treatment (cover crop vs. control) and current week with individual plant ID as the random effect. We then tested our model with an ANOVA using the anova() function in base R and significant interactions were subjected to post-hoc analyses using the emmeans() function in the emmeans package (Lenth et al. 2022), where all pair-wise comparisons used a Tukey’s p-value adjustment method.

$$RGR for Week n=\frac{Height (Week n+1)-Height \left(Week n\right)}{Height \left(Week n\right)}$$

Equation 5. Calculation for weekly relative growth rate (RGR).

We assessed photosynthetic efficiency (F_VM) in both vegetative (V5-V7) and reproductive stages (R3) by creating a linear mixed effects model using the lme() function in the nlme package (Pinheiro et al. 2021), where photosynthetic efficiency (F_VM) was a function of treatment (cover crop vs. control) and plant stage (vegetative vs. reproductive) with the individual plant ID as a random effect. We again tested our model with an ANOVA using the anova() function in base R and significant interactions were subjected to post-hoc analyses using the emmeans() function in the emmeans package (Lenth et al. 2022), where all pair-wise comparisons used a Tukey’s p-value adjustment method.

We quantified differences between corn reproductive development when planted on a plot with a cover crop application compared to a plot left fallow by assessing final ear count, kernel count per plant, and kernel weight per plant. For final ear count, we created a generalized linear model with a Gaussian distribution that better captured ears where only half the kernels developed, using the glm() function in base R, where ear count was a function of treatment (cover crop vs. control). Final kernel count was assessed with a generalized linear model with a Poisson distribution using the glm() function in base R, with kernel count as a function of treatment (cover crop vs. control). Differences in final kernel weight were assessed with a linear model using the lm() function in base R, where kernel weight was a function of treatment (cover crop vs. control). All corn reproductive models were tested with an ANOVA using the anova() function in base R. We then performed post-hoc analyses using the emmeans() function in the emmeans package (Lenth et al. 2022), where all pair-wise comparisons used a Tukey’s p-value adjustment method for all significant interactions.

Furthermore, since corn yield can encompass both kernel production and the whole plant (silage harvest), yield was also captured with above ground, below ground, and total plant biomass measurements. To evaluate differences in biomass, we created three separate linear models using the lm() function in base R, with biomass (above ground, below ground, or total) as a function of treatment (cover crop vs. control) and these models were used for both silage and final harvest. All biomass models were tested with an ANOVA using the anova() function in base R, and any significant interactions were tested using post-hoc analyses using the emmeans() function in the emmeans package (Lenth et al. 2022) where all pair-wise comparisons used a Tukey’s p-value adjustment method.

Finally, we assessed the relationship of crop responses associated with production (biomass, kernel count) with individual soil properties. We created linear models using the lm() function in base R, where total biomass was a function of a single soil property (Physical: % sand, % silt, % clay, % OM, BD, moisture; Chemical: pH, CEC, N, P, K, Mg⁺, Ca⁺; Enzyme Activity: AKP, BG, LAP, PER, PPO, and UR) and treatment (cover crop vs. control). We used generalized linear models using the glm() function in base R for kernel count with a Poisson distribution, where kernel count was a function of a single soil property. We then tested each model with an ANOVA using the anova() function in base R, and any significant interactions were assessed using the emmeans() function in the emmeans package (Lenth et al. 2022), where all pair-wise comparisons used a Tukey’s p-value adjustment method.

Soil physiochemical properties

Soil pH significantly changed over time and was dependent on cover crop treatment (F_2,36 = 40.00, P < 0.0001, R² = 0.86, Fig. 1A). While soil pH was initially higher in the plot with the cover crop compared to the control plot, pH increased at post-cover crop collection for both plots so that they were statistically indistinguishable from each other (Fig. 1A). By post-harvest, overall soil pH decreased slightly when compared to the spring collection time but remained statistically indistinguishable between plots (Fig. 1A). Finally, soils from the cover crop treatment had the same pH at the pre-cover crop and post-harvest collection times, while soil pH in the control plot increased over the same period (Fig. 1A).

Differences in soil texture were dependent on treatment but not time. We found a significant decrease in the % sand (F_1,18 = 10.35, P = 0.0048, R² = 0.15, Fig S1A) in the cover crop plot compared to the control plot, independent of time. In contrast, for % clay, there was a significant difference in the cover crop plot compared to the control plot, independent of time (F_1,18 = 2.7380, P = 0.0135, R² = 0.11, Fig S1B). We found no significant differences in % silt (P = 0.1141), moisture content (P = 0.7984) or BD (P = 0.9597) between the interaction of treatment and time, or by time (P > 0.05), or treatment (P > 0.05) independently.

Soil texture (% sand, % silt, % clay), BD, moisture, and pH were all condensed into two principal components, which explained 24.8% and 34.4% of the total variation in the data. The analysis of similarity on the resulting two linear principal components revealed significant differences in soil properties based on both treatment and time, separately (P = 0.003, R² = 0.14 Fig. 1B; P = 0.004, R² = 0.11, Fig. 1C); however, the interaction of treatment and time was not significant (P = 0.210).

When assessing differences in chemical properties of the soil by treatment over time, we found no significant effects of treatments on soil CEC (P = 0.1054) or the K content (P = 0.6504), Mg⁺ (P = 0.2899), and Ca⁺ (P = 0.0942). Similarly, % OM did not change with the use of a cover crop over time (P = 0.1886). Total N (P = 0.8134) and total P (P = 0.5323) also remained stable between treatments over time. The soil chemical properties of CEC, Mg⁺, Ca⁺, K, N, P, and OM were all condensed into two principal components that explained 33.8% and 39.2% of the variation within this data. An analysis of similarity discerned differences in soil chemical properties based on time (P = 0.001, R² = 0.85, Fig. 2) but not cover crop use (P = 0.626) or their interaction (P = 0.958).

Enzyme Activities

Differences in enzyme activities due to cover crop treatment and time were enzyme specific. LAP activity significantly changed with collection time by cover crop use (F_2,36 = 7.277, P = 0.0022, R² = 0.54, Fig. 3A). During the pre-cover crop collection time, LAP activity was greater in the plot designated for cover crop planting compared to the control plot left fallow (Fig. 3A). Over the winter, LAP activity increased in the control plot but remained the same in the plot with cover crop application, removing any differences between plots during the post-cover crop collection time (Fig. 3A). However, by post-harvest collection, overall LAP activity decreased in both plots, maintaining no differences between treatments (Fig. 3A). AKP activity did not significantly change by treatment (cover crop vs. control) over collection periods (P = 0.7930); however, AKP activity increased over collection period alone (F_2,38 = 23.92, P < 0.0001, Fig. 3B). PPO activity did not differ by treatment with collection time (P = 0.1404) but was greater in the cover crop plot compared to the control plot, independent of time (F_1,18 = 7.250, P 0.0149, R² = 0.11, Fig. 3C). We found no significant differences in BG (P = 0.8021), PER (P = 0.1215), or UR (P = 0.5272) activities between treatments over time or independent of time (P > 0.05).

AKP, BG, LAP, PER, PPO, and UR activities were condensed into two principal components that explained 20.6% and 30.3% variation within the data. The analysis of similarity on the resulting two linear principal components discerned significant differences in soil enzyme activities based on the use of a cover crop (P = 0.031, R² = 0.05, Fig. 4A) and time (P = 0.013, R² = 0.10, Fig. 4B); however, the interaction of treatment and time was not significant (P = 0.262).

Plant Responses

Plant development varied with the use of a cover crop (F_{1, 21}= 1.654, P = 0.0454, R² = 0.99, Fig. 5A), with corn in the control plot taking longer to reach plant stages V5 and V6, while corn in the cover crop plot taking longer to reach V11 (Fig. 5A). There was no difference between corn grown in the cover crop applied plot and the control plot left fallow over time for RGR (P = 1.000, Fig S2), plant height (P = 0.2242) or leaf count (P = 0.9998), or by treatment or week independently (P > 0.05).

Photosynthetic efficiency was ~ 56% greater during vegetative development for the control plot compared to the plot with the cover crop application; however, this difference was lost once corn reached reproductive stages in the control plot as photosynthetic efficiency decreased (F_3,35 = 23.34, P < 0.0001, R² = 0.64, Fig S3).

During both harvests, corn biomass allocation after cover crop application had a ~ 65% smaller root to shoot ratio than corn grown in the control plot left fallow over the winter (silage harvest: F_1,18 = 5.458, P = 0.0312, R² = 0.19, Fig. 5B; final harvest: F_1,18 = 6.150, P = 0.0233, R² = 0.21, Fig. 5C). However, despite these differences in biomass allocation, we found no significant differences in above ground (silage harvest: P = 0.4364; final harvest: P = 0.0671), below ground (silage harvest: P = 0.0844; final harvest: P = 0.0894) or total plant biomass (silage harvest: P = 0.5638; final harvest: P = 0.0972) between treatments at harvest.

Corn grown after cover crop application had a significant increase in both the number of ears (F_1,18 = 1.525, P = 0.015, R² = 0.29, Fig. 5D) and kernel count per plant (F_1,18 = 15.00, P < 0.0001, R² = 0.32, Fig. 5E) compared to corn grown in the control plot after a fallow season; however, kernel count per ear (P = 0.618), kernel weight per ear (P = 0.157) and kernel weight per plant (P = 0.1749) were not significantly different between treatments.

Soil properties and plant responses

The impact of soil chemistry on plant growth was dependent on treatment, with several soil chemical properties significantly influencing final kernel count and total plant biomass. We found that CEC (F_3,2 = 9.369, P = 0.0384, R² = 0.83, Fig S4A), Ca⁺ (F_3,2 = 20.43, P = 0.0470, R² = 0.92, Fig S4B) and pH (F_3,16 = 4.134, P = 0.0239, R² = 0.33, Fig S4C) significantly impacted plant biomass as a function of cover crop application such that when the soil property increases, the total biomass of the plant from the control plot increases while total biomass in plants from the cover crop plot decreases.

Six of the physical and chemical soil properties we assessed influenced kernel count. For example, as N increased, kernel count increased for the corn grown after the cover crop, while kernel count decreased for corn grown in the control plot (F_5,8 =16.03, P < 0.0001, R² = 0.67, Fig. 6A). Additionally, as K increased, the kernel count of corn grown after the cover crop application also increased; however, the kernel count of corn grown in the control plot decreased (F_2,5 = 11.73, P = 0.0115, R² = 0.87, Fig. 6B). Total P (F_5,8 =13.52, P < 0.0001, R² = 0.66, Fig. 6C), CEC (F_2,5 = 1.840, P = 0.0069, R² = 0.98, Fig. 6D), Ca⁺ (F_2,5 = 1.821, P = 0.0024, R² = 0.98, Fig. 6E), BD (F_2,18 = 6.601, P = 0.0101, R² = 0.37, Fig. 6F) and % silt (F_2,18 = 38.16, P < 0.0001, R² = 0.40, Fig. 6G) all had similar effects such that as the soil property increased, the kernel count for corn grown in the control plot increased, whereas the kernel count for the corn grown in the cover crop plot decreased.

Soil enzyme activities also drove differences in kernel count. As BG activity increased, kernel count in the cover crop and control plots decreased (F_2,18 = 10.19, P = 0.0014, R² = 0.40, Fig. 6H). As LAP activity increased, kernel count increased for corn grown on the cover crop plot, whereas kernel count for corn grown in the control plot decreased (F_2,18 = 42.53, P < 0.0001, R² = 0.39, Fig. 6I). Finally, as PER activity (F_2,18 = 9.601, P = 0.0019, R² = 0.44, Fig. 6J) and UR activity (F_2,18 = 7.423, P = 0.0065, R² = 0.64 Fig. 6K) increased, the kernel count of corn grown in both the cover crop and control plots decreased.

Cover crop application has gained momentum as a beneficial soil management practice, doubling in use from 3% of all agricultural land in 2012 to 7.3% in 2021 (Hellerstein et al. 2019; Wallander et al. 2021). This increase is directly related to the wide variety of benefits cover crops provide, including: enhancing soil structure, increasing organic matter, reducing soil erosion, and promoting pest management (Gabriel and Quemada 2011; Steele et al. 2012; Kim et al. 2020; Wittwer and van der Heijden 2020). Additionally, previous research has shown that combining cover crop application with soil amendments further increased soil stability, crop production, and yields (Fernandez et al. 2016; Sánchez de Cima et al. 2016; Ashworth et al. 2017; Adeli et al. 2019; Raut et al. 2020). However, despite these benefits, the effect of using a cover crop with dredged sediments on soil properties and its subsequent relationship with crop production is still largely unknown. Here we show that using a cover crop on dredged sediments increased corn yields compared to corn grown on dredged sediments left fallow. We further show that this effect was mediated through increased nutrient mineralization as a direct result of increased microorganism-associated enzyme activities.

Nutrient Dynamics Drive Corn Biomass and Production

Cover crop application positively influenced the relationship between N cycling properties and corn yields. Following cover crop application, we found a positive relationship between total kernel count and total N content as well as the enzymatic activities associated with N mineralization (LAP, PER, and UR). However, we failed to find a significant increase in total plant biomass with N mineralization-associated enzymes or total N content. The increase in kernel count, but not biomass, suggests that cover crop application promotes the conversion of bioavailable N for plant uptake beneficial for increased kernel count by increasing microorganism enzymatic activities associated with nutrient mineralization. Despite our findings aligning with previous findings in agricultural systems (Jayasundara et al. 2007; Nyiraneza et al. 2010) the use of winter rye cover crop provided beneficial relationships different than those previously described following winter rye use. Specifically, we found that corn yields increased with increased N mineralization following cover crop application, which contrasts previous research using winter rye (Chim et al. 2022). The increases in kernel count driven by N-mineralization-associated enzyme activities supports our hypothesis for coupling a cover crop with dredged sediments for agricultural use, which is the goal of using soil amendments in agricultural systems.

Although we found significant relationships between soil properties related to N mineralization and increases in kernel count following cover crop application, we failed to find similar relationships for soil properties related to P cycling. Specifically, total kernel count decreased as total P content increased following cover crop application, and there was no significant relationship found between total P content and plant biomass. This negative relationship between kernel production and total P content and lack of relationship between plant biomass and total P content or AKP activity was surprising as P is vital for corn plant growth and kernel production (Hellerstein et al. 2019; USDA 2019). Additionally, there was no significant relationship between total kernel count or plant biomass and AKP activity, which is associated with P mineralization. The lack of change in AKP activity suggests the microorganism community is failing to mineralize the cover crop-driven increase in total P, making it unavailable for plant uptake. This mirrors other cover crop research in which the P content in soils can either be taken up directly by plants (Mori et al. 2023) or used by microorganisms in other ways such as increased biomass (Hallama et al. 2019). Another potential explanation for high total soil P content coupled with a lack of AKP activity, is that P may be fixed within the soil, potentially bound to heavy metals creating non-bioavailable forms such as iron phosphate (USDA 2019; Durrer et al. 2021). The lack of any positive relationship between kernel production and P cycling suggests that cover crops applied to dredged sediments may not support P mineralization needed for increasing kernel production. However, to evaluate the relationship of the P cycle dynamics in dredged sediments more effectively, future research should focus on P uptake in plants and how those patterns differ from uptake in traditional agricultural systems.

The relationship between the breakdown of OM into mineralized C and total kernel count was directly linked to the application of a cover crop; however, we failed to establish this connection with plant biomass. Overall, we found an inverse relationship between BG activity and total kernel count, indicating that an increase in the breakdown of OM does not translate into increased corn production. The trade-off suggests that there is a threshold for mineralized C within agricultural soil systems, and higher levels of OM breakdown due to cover crop application may not be necessary to increase corn yields (Aon and Colaneri 2001). This tradeoff mimics other research which found a negative effect on BG activity following cover crop application in non-amended agricultural soils (Bandick and Dick 1999) and non-dredged sediment amended agricultural soils (Ashworth et al. 2017). However, these findings contradict evidence that applying a cover crop alters the C cycle within soils by providing mineralized C for use within agricultural systems (Schipanski et al. 2014; Aldridge et al. 2019). This disparity may have been caused by the fact the cover crop used in this study did not grow to senescence but was tilled under while still growing. Generally, microorganisms can form microaggregates from OM provided by a cover crop, sequestering C within the soil (Gougoulias et al. 2014); however, this relationship was not seen in our study, likely due to the life-cycle of the cover crop. While our results suggest that cover crop applications are not necessary for increasing kernel production as dredged sediments alone change C cycling in ways sufficient to increase corn yield, future research should evaluate the relationship between multiple cover crop growing seasons and corn grown on dredged sediments to understand how the C cycle dynamics are impacted.

In addition to nutrient cycling, cover crop application on dredged sediments drives changes in the relationships between other chemical properties and plant responses. Our results indicate an inverse relationship between both total kernel count and total plant biomass to Ca⁺ content, CEC, and soil pH with cover crop application. This suggests high levels of Ca⁺ are not needed for kernel production and higher cations levels and soil pH are not conducive to increase plant biomass; however, these patterns oppose those found in non-dredged sediment amended agricultural systems (Tarkalson et al. 2006). The positive relationship between K content and total kernel count with cover crop application contrasts both the relationships with other cations within this study as well as previous research where higher K content reduced overall corn production in both traditionally farmed agricultural systems (Rhem 1994) and cover crop applied systems (Jian et al. 2020). Total plant biomass did not demonstrate any significant relationship with K content. Taken together, these findings suggest increased soil pH, Ca⁺ content, and CEC in dredged sediments due to cover crop application are not necessary for overall kernel production or plant biomass. Consequently, based on changes in soil chemistry, cover crop application on dredged sediments may not be necessary to improve overall plant development. These outcomes were surprising as soil chemistry is typically an important driver of plant development in agricultural soils (Russell et al. 2006; Gaspar 2019).

Similar patterns emerged when quantifying the relationships between physical or chemical soil properties and plant responses, which is not surprising as these properties themselves are interconnected. Total kernel count decreased with increasing physical properties of BD and silt content due to cover crop application; however, this relationship did not extend to plant biomass. These results suggest higher BD is bad for kernel production when grown on dredged sediments following cover crop application, which is unusual in agricultural systems; previous studies have shown that generally corn production on agricultural soils decreases when BD declines following cover crop application (Koudahe et al. 2022). This differential response in the relationship between BD and kernel production with cover crop application suggests there are overall differences in soil stability between agricultural soil and dredged sediments (Marlin 2002; Vermeulen et al. 2005; Oliveira et al. 2017). The negative relationship following cover crop application between BD and kernel production shows cover crop application is unnecessary to change BD to increase crop production.

Cover Crop Application Alone Drives Corn Yield Increases

Several corn responses increased directly due to the application of a cover crop but were not mediated by physical, chemical, or biological soil properties. Corn grown in the cover crop applied plot produced 1.5-2x more ears compared to the plot left fallow. In addition to the increased corn yields following cover crop application, the combined corn yield from both experimental plots from our dredged sediment experiment was 20% greater than surrounding agricultural fields (Lucas County, Ohio; NASS USDA 2020). The increased yields from corn grown in dredged sediments lead to differences in biomass allocation between treatment plots.

Plant biomass allocation, but not total biomass, was influenced by the application of a cover crop. Differences in plant biomass distribution is typical in low nutrient environments, where plants require more roots compared to high-nutrient environments, in which plants distribute biomass towards reproduction (Poorter et al. 2012). Similarly, in our study, corn in the fallow control plot developed ~ 65% more roots to obtain the necessary nutrients that cover crops aid in providing to promote corn production, which aligns with plant allocation patterns typically found in agricultural systems (Postma et al. 2014; Gabriel and Quemada 2011). These findings demonstrate that combining a cover crop and dredged sediments can increase overall corn production.

The time it took to reach a vegetative or reproductive stage (plant stage development) varied in response to cover crop application but was not driven by changing soil properties. Specifically, the time to reach plant stage V5 and V6 was significantly faster in the cover crop applied plot while V11 was reached faster in the control plot left fallow. However, the time to reach any of the reproductive stages was did not significantly vary. We found no significant differences in either RGR, the number of leaves or height for the corn, as vegetative stages are representative of developed leaves and RGR is representative of plant height. Altogether, our results suggest that when cover crops are applied to dredged sediments they can enhance early corn growth and development, which align with corn responses to cover crop application in non-dredged sediment-supplemented agricultural soil systems (Abendroth et al. 2011; Gabriel and Quemada 2011; Steele et al. 2012). However, the short-lived differences in plant stage development between the cover crop and fallow treatments suggest cover crop application may not be needed to stimulate corn growth when coupled with dredged sediments.

In this study, corn production varied following cover crop application on dredged sediments. There was a significant increase in corn yield following cover crop application compared to the control plot left fallow. However, despite the yield differences between the cover crop and non-cover crop plots, we failed to find meaningful differences in plant growth and development for plant height, RGR, leaf count, or plant stage development. Additionally, cover crop application was not beneficial for photosynthetic efficiency as efficiency was significantly greater in the control plot left fallow during vegetative development and decreased over time regardless of treatment. Responses for corn grown in dredged sediments following cover crop application varied from those documented within non-dredged sediment amended agricultural systems, with many benefits missing (Mahama et al. 2016; Acharya et al. 2020; Chim et al. 2022). Although corn yield was the only plant response to benefit from cover crop application due to changes in soil properties, the combination of cover crop application with dredged sediments may be beneficial but not necessary to improve corn production.

Soil Properties Differ in Response to Cover Crop Application

Several dredged sediment soil properties failed to significantly vary between the cover crop and non-cover crop plots. Total N, UR activity, and LAP activity remained consistent throughout the growing season, regardless of cover crop application, suggesting constituents of the N cycle are unaltered by the application of a cover crop in dredged sediments. This pattern is opposite to other patterns for non-dredged sediment agricultural soil systems where N mineralization rates increase following cover crop application (Christopher et al. 2021). Additionally, LAP activity, which is linked to both N and C mineralization through the breakdown of protein in soils (Greenfield et al. 2021), remained unchanged in the plot left fallow but decreased over time in the cover crop plot. These results suggest cover crop application aids dredged sediments in maintaining sufficient nutrient availability, leading to a reduced need for microorganism associated LAP activity (Gabriel and Quemada 2011; McDaniel et al. 2014; Wittwer and van der Heijden 2020).

Overall, there were varied responses in the soil properties within the C cycle between plots. Specifically, there were no differences between the treatment plots for OM content, or BG and PER activity, which are two enzymes associated with OM breakdown to mineralized C. However, PPO activity, which is responsible for more complex C breakdown, significantly increased within the cover crop applied plot compared to the plot left fallow, regardless of time. Increases in PPO activity may suggest that as C becomes limited in the sediments, it may be necessary for the breakdown of more complex OM provided by a cover crop (Aon and Colaneri 2001; Hungria et al. 2009; Jamir et al. 2019). The lack of difference in total OM content between the treatments coupled with the increases in PPO activity differed from results in most agricultural soils systems where cover crop application is known to increase OM content (Hellerstein et al. 2019; Acharya et al. 2020). These patterns together suggest cover crop application on dredged sediments is not necessary to alter OM content due to the already high amount of OM found in dredged sediments (Sigua 2005; Oliveira et al. 2017; Kiani et al. 2021).

Constituents of the P cycle also did not vary between the cover crop and non-cover crop plots. Total P content remained consistent throughout the growing season, regardless of cover crop application. However, AKP activity, which is associated with P mineralization, increased over time regardless of cover crop application. These findings suggest that regardless of cover crop application, mineralized P is limited in dredged sediments throughout a growing season (Hallama et al. 2019), which mirrors typical outcomes in agricultural soil systems with (Zhu et al. 2018; Hellerstein et al. 2019) or without cover crop application (Hou et al. 2012; Zhu et al. 2018; Hellerstein et al. 2019). The results from this study suggest that using dredged sediments in agricultural systems coupled with cover crop applications may not address the limited P issues currently in agriculture. Since this study was only over one growing season, additional research investigating multiple growing seasons may provide further insight.

While moisture content remained consistent between the cover crop applied plot and the plot left fallow, this outcome has unique context. Dredged sediments are known to have high water holding capacity, as they are sediments originally removed from an aquatic environment (Averett et al. 1990), so there is significant concern about their ability to retain water during high precipitation events (Vermeulen et al. 2005). Our study took place in Toledo, Ohio, USA (Lucas County) following several severe weather events between November 2018 through April 2019 that caused the United State Department of Agriculture to declare a natural disaster area (USDA 2019). The study site and surrounding county experienced 72% more rainfall in November 2018 compared to the 30-year average, followed by extreme cold, dry, and windy weather from the polar vortex from January until April, and concluding with another period of intense precipitation during which the region experienced a 37% increase in rainfall compared to the 30-year average in both March and April 2019 (USDA 2019; Toledo Weather Records 2019). Despite these circumstances, soil moisture content remained consistent at ~ 30% from pre-cover crop to post-cover crop collection periods and decreased over the corn growing season, regardless of cover crop application. These findings support previous research showing that dredged sediments can withstand significant precipitation events and not become oversaturated (Vermeulen et al. 2005; Oliveira et al. 2017).

The ability of dredged sediments to maintain consistent moisture content is essential for their use in agricultural systems, as changes in moisture content are linked to changes in soil chemistry (Rengasamy et al. 2022). The CEC and associated cations K, Ca⁺, and Mg⁺ all remained consistent within the plots, regardless of cover crop application. Additionally, soil pH also remained consistent at the high end of the neutral range for corn, between 7.5-8, regardless of cover crop application. This consistency in soil chemistry supports the hypothesis that dredged sediments, regardless of cover crop application, can create and maintain a hospitable chemical environment for corn production (Oliveira et al. 2017; Darmody and Diaz 2017). This is particularly valuable since the slightly basic dredged sediments could be used to neutralize agricultural soils which have become increasingly acidic due to repeated synthetic fertilizer application (Darmody and Marlin 2002; Ozlu and Kumar 2018; Iqbal et al. 2021).

Aligning with the patterns seen within the soil chemistry, dredged sediment structure and stability remained unchanged over time regardless of cover crop application. BD remained consistent at 0.8803 ± 0.0106 g/ml^− 1 regardless of cover crop application, which is just below the USDA recommended range of 1.0 -1.4 g/ml^− 1 (NRCS USDA 2019). The consistency in BD indicates dredged sediments can maintain soil porosity regardless of cover crop application, which is necessary for nutrient and water retention and vital for root development (Rhem 1994; Arshad et al. 1997; Jiang and Thelen 2004; Daniels et al. 2007; Darmody and Diaz 2017; Gaspar 2019). The ratio of soil textures did not change with the addition of a cover crop, and initial texture ratios were maintained throughout the corn growing season. No change in soil texture is consistent with prior research showing the texture of dredged sediments remains stable in field applications, with or without cover crop applications (Villamil et al. 2006; Steele et al. 2012; Darmody and Diaz 2017). The ability of dredged sediments to maintain their physical soil structure and stability regardless of cover crop application supports prior research demonstrating dredged sediments’ physical properties often align with agricultural standards (Sigua 2005; Daniels et al. 2007; Darmody and Diaz 2017).

Cover crop application has the potential to increase crop yields when coupled with dredged sediments and has many additional benefits in agricultural soil systems, including the improvement of BD and water content, microbial-driven nutrient mineralization, and crop yield increases (Gabriel and Quemada 2011; Steele et al. 2012; Hellerstein et al. 2019; Kim et al. 2020; Wittwer and van der Heijden 2020; Acharya et al. 2020). Within this study, cover crop had little influence on soil properties, with only the most notable relationship found in microbial-driven enzymatic activities leading to increased corn yields. The differences in outcomes from cover crop application may be due to the specific cover crop we used in this study to reflect local growing practices, winter rye, which has been known to only moderately improve soil properties and actually decrease overall corn production (Acharya et al. 2020). Consequently, the results from our study may not fully reflect the potential for cover crops to improve dredged sediments for agricultural use but instead reflect the unique effect of using winter rye prior to planting corn.

In investigating the impact of cover crop application on dredged sediments, corn yields increased following cover crop application compared to dredged sediments left fallow. Yield increases were mediated through increased microorganism-associated enzyme-driven nutrient mineralization, specifically N mineralization, which was not evident in the plot left fallow. However, we failed to find positive relationships between either kernel count or plant biomass and most other soil physical, biological, and chemical properties following cover crop application. Together, soil property and plant responses indicate that dredged sediments, especially in combination with a cover crop, have the potential to increase crop yields while maintaining soil stability and microorganism activity compared to traditionally farmed agricultural soils (Sigua 2005; Darmody and Diaz 2017; Villamil et al. 2006). Furthermore, dredged sediments, regardless of cover crop application, demonstrated a consistent water holding capacity even when inundated with excessive precipitation events. The work presented here represents an important step in advancing dredged sediment research and expanding current agricultural practices to further increase crop production and potentially repair degraded agricultural soils.

Statements & Declarations

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Funding

This research was financially supported by the Ohio Lake Erie Commission – Lake Erie Protection Fund (Grant No. SG 540-2019).

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author Contributions

Megan A. Rúa and Louise Stevenson were responsible for the experimental design. Ashley N. Julian, Megan A. Rúa and Louise Stevenson collected the data. Ashley N. Julian performed the analyses and wrote the first draft of the manuscript. All authors reviewed and approved the final manuscript.

Data Availability

The data to support the findings within this experiment have been made available in the Environmental Data Initiative (EDI) repository (Julian et al. 2023). All additional data available upon request.

Acknowledgements

The research team would like to thank Sandra Kosek-Sills and Lynn Garrity at the Ohio Lake Erie Commission, who coordinated this project, and David Emerman and Vanessa Steigerwald Dick from the Ohio Environmental Protection Agency who advised the research team throughout the course of the project. Joe Cappel at the Toledo Port Authority who provided access to the GLDMCI and Bob Klumm a local farmer who prepped the site. Wright State undergraduates Emily Kahlert, Lea Kelty, Sarah Krafcik, Michael McKean, Carson Richardson, and Taylor Ross who helped with data collection in the field and laboratory, graduate students Dr. Molly Simonis and Dr. Michaela Woods who reviewed earlier drafts of this manuscript, and Dr. Don Cipollini for the pre-submission review.

Abendroth, L. J., R. W. Elmore, M. J. Boyer, and S. K. Marlay. 2011. “Effect of Rye or Oat Cover Crop on Corn Growth and Development in a Rain-Fed, Wheat-Corn Rotation.” Journal of Environmental Quality, Corn Growth and Development, 50 (1). https://doi.org/10.1002/jeq2.20217.
Acharya, Jyotsna, Thomas B. Moorman, Thomas C. Kaspar, Andrew W. Lenssen, and Alison E. Robertson. 2020. “Cover Crop Rotation Effects on Growth and Development, Seedling Disease, and Yield of Corn and Soybean.” Plant Disease 104 (3): 677–87. https://doi.org/10.1094/PDIS-09-19-1904-RE.
Adeli, Ardeshir, John P. Brooks, John J. Read, Gary Feng, Dana Miles, Mark W. Shankle, and Johnie N. Jenkins. 2019. “Corn and Soybean Grain Yield Responses to Soil Amendments and Cover Crop in Upland Soils.” Journal of Plant Nutrition 42 (19): 2484–97. https://doi.org/10.1080/01904167.2019.1655046.
Aldridge, C. A., B. H. Baker, and A. R. Omer. 2019. “Investigation of Short-Term Effects of Winter Cover Crops on Compaction and Total Soil Carbon in a Long-Term No-till Agricultural System.” Journal of Soil and Water Conservation 74 (1): 77–84. https://doi.org/10.2489/jswc.74.1.77.
Al-Kaisi, Mahdi, and David Kwaw-Mensah. 2007. “Effect of Tillage and Nitrogen Rate on Corn Yield and Nitrogen and Phosphorus Uptake in a Corn-Soybean Rotation.” Agronomy Journal 99 (6): 1548–58. https://doi.org/10.2134/agronj2007.0012.
Aon, M.A, and A.C Colaneri. 2001. “II. Temporal and Spatial Evolution of Enzymatic Activities and Physico-Chemical Properties in an Agricultural Soil.” Applied Soil Ecology 18 (3): 255–70. https://doi.org/10.1016/S0929-1393(01)00161-5.
APHIS USDA. 2020. “USDA APHIS | Additional Guidance on Agronomic Performance Data of Corn.” 2020. https://www.aphis.usda.gov/aphis/ourfocus/biotechnology/permits-notifications-petitions/sa_guidance_documents/ct_cornguidance.
Arshad, M. A. (Charlie), Birl Lowery, and Bob Grossman. 1997. “Physical Tests for Monitoring Soil Quality.” In Methods for Assessing Soil Quality, 123–41. John Wiley & Sons, Ltd. https://doi.org/10.2136/sssaspecpub49.c7.
Ashworth, Amanda J., Fred L. Allen, Arnold M. Saxton, and Donald D. Tyler. 2017. “Impact of Crop Rotations and Soil Amendments on Long-Term No-Tilled Soybean Yield.” Agronomy Journal 109 (3): 938–46. https://doi.org/10.2134/agronj2016.04.0224.
Averett, Daniel E., Bret D. Perry, Elizabeth J. Torrey, and Jan A. Miller. 1990. “Review of Removal, Containment and Treatment Technologies for Remediation of Contaminated Sediment in the Great Lakes:” Fort Belvoir, VA: Defense Technical Information Center. https://doi.org/10.21236/ADA233946.
Bandick, Anna K, and Richard P Dick. 1999. “Field Management Effects on Soil Enzyme Activities.” Soil Biology and Biochemistry, 9.
Baniulyte, Dovile, Emmanuel Favila, and John J. Kelly. 2009. “Shifts in Microbial Community Composition Following Surface Application of Dredged River Sediments.” Microbial Ecology 57 (1): 160–69. https://doi.org/10.1007/s00248-008-9410-y.
Barbarick, K. A., J. A. Ippolito, J. McDaniel, N. C. Hansen, and G. A. Peterson. 2012. “Biosolids Application to No-till Dryland Agroecosystems.” Agriculture, Ecosystems & Environment 150 (March): 72–81. https://doi.org/10.1016/j.agee.2012.01.012.
Bates, Douglas, Martin Maechler, Ben Bolker [aut, cre, Steven Walker, Rune Haubo Bojesen Christensen, Henrik Singmann, et al. 2022. “Lme4: Linear Mixed-Effects Models Using ‘Eigen’ and S4.” https://CRAN.R-project.org/package=lme4.
Baumhardt, R. L., B. A. Stewart, and U. M. Sainju. 2015. “North American Soil Degradation: Processes, Practices, and Mitigating Strategies.” Sustainability 7 (3): 2936–60. https://doi.org/10.3390/su7032936.
Biggelaar, Christoffel den, Rattan Lal, Keith Wiebe, and Vince Breneman. 2001. “Impact of Soil Erosion on Crop Yields in North America.” In Advances in Agronomy, 72:1–52. Elsevier. https://doi.org/10.1016/S0065-2113(01)72010-X.
Canet, R, C Chaves, F Pomares, and R Albiach. 2003. “Agricultural Use of Sediments from the Albufera Lake (Eastern Spain).” Agriculture, Ecosystems & Environment 95 (1): 29–36. https://doi.org/10.1016/S0167-8809(02)00171-8.
CFAES. 2022. Weather System. 2022. https://weather.cfaes.osu.edu/frostdates.asp?id=1%20class=.
Chim, Bee Khim, Shannon L. Osborne, and R. Michael Lehman. 2022. “Short-Term Corn Yield Response Associated with Nitrogen Dynamics from Fall-Seeded Cover Crops under No-till Dryland Conditions.” Agrosystems, Geosciences & Environment 5 (3): e20305. https://doi.org/10.1002/agg2.20305.
Christopher, Sheila, Jennifer Tank, Ursula Mahl, Brittany Hanrahan, and Todd Royer. 2021. “Effect of Winter Cover Crops on Soil Nutrients in Two Row‐cropped Watersheds in Indiana (USA).” Journal of Environmental Quality 50 (March). https://doi.org/10.1002/jeq2.20217.
Crookston, R. K., J. E. Kurle, P. J. Copeland, J. H. Ford, and W. E. Lueschen. 1991. “Rotational Cropping Sequence Affects Yield of Corn and Soybean.” Agronomy Journal 83 (1): 108–13. https://doi.org/10.2134/agronj1991.00021962008300010026x.
Daniels, W. Lee, Richard Whittecar, and Charles H. Carter III. 2007. “CONVERSION OF POTOMAC RIVER DREDGE SEDIMENTS TO PRODUCTIVE AGRICULTURAL SOILS.” Journal American Society of Mining and Reclamation 2007 (1): 183–99. https://doi.org/10.21000/JASMR07010183.
Darmody, Robert G, and Dorivar Ruiz Diaz. 2017. “Dredged Sediment: Application as an Agricultural Amendment on Sandy Soils.” Illinois Sustainable Technology Center, 103.
Darmody, Robert G, and John C Marlin. 2002. “Sediments and Sediment-Derived Soils in Illinois: Pedological and Agronomic Assessment.” Environmental Monitoring and Assessment 77: 19.
Durrer, Ademir, Thiago Gumiere, Maurício Rumenos Guidetti Zagatto, Henrique Petry Feiler, Antonio Marcos Miranda Silva, Rodrigo Henriques Longaresi, Sérgio K. Homma, and Elke J.B.N. Cardoso. 2021. “Organic Farming Practices Change the Soil Bacteria Community, Improving Soil Quality and Maize Crop Yields.” PeerJ 9 (September): e11985. https://doi.org/10.7717/peerj.11985.
ERS USDA. 2019. “USDA ERS - Fertilizers & Pesticides.” 2019. https://www.ers.usda.gov/topics/farm-practices-management/fertilizers-pesticides/.
Eswaran, H., R Lal, and P.F. Reich. 2001. “Land Degradation: An Overview | NRCS Soils.” 2001. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/use/?cid=nrcs142p2_054028.
Fernandez, Adria L., Craig C. Sheaffer, Donald L. Wyse, Christopher Staley, Trevor J. Gould, and Michael J. Sadowsky. 2016. “Structure of Bacterial Communities in Soil Following Cover Crop and Organic Fertilizer Incorporation.” Applied Microbiology and Biotechnology 100 (21): 9331–41. https://doi.org/10.1007/s00253-016-7736-9.
Fernández, José M., César Plaza, Juan C. García-Gil, and Alfredo Polo. 2009. “Biochemical Properties and Barley Yield in a Semiarid Mediterranean Soil Amended with Two Kinds of Sewage Sludge.” Applied Soil Ecology 42 (1): 18–24. https://doi.org/10.1016/j.apsoil.2009.01.006.
Filiberto, David M., and John L. Gaunt. 2013. “Practicality of Biochar Additions to Enhance Soil and Crop Productivity.” Agriculture 3 (4): 715–25. https://doi.org/10.3390/agriculture3040715.
Fonseca, R., F.J.A.S. Barriga, and W.S. Fyfe. 1998. “Reversing Desertification by Using Dam Reservoir Sediments as Agriculture Soils.” Episodes 21 (4): 218–24. https://doi.org/10.18814/epiiugs/1998/v21i4/001.
Foster, Erika J., Neil Hansen, Matt Wallenstein, and M. Francesca Cotrufo. 2016. “Biochar and Manure Amendments Impact Soil Nutrients and Microbial Enzymatic Activities in a Semi-Arid Irrigated Maize Cropping System.” Agriculture, Ecosystems & Environment 233 (October): 404–14. https://doi.org/10.1016/j.agee.2016.09.029.
Fox, John, Sanford Weisberg, Brad Price, Daniel Adler, Douglas Bates, Gabriel Baud-Bovy, Ben Bolker, et al. 2022. “Car: Companion to Applied Regression.” https://CRAN.R-project.org/package=car.
Francy, Donna S., Amie M.G. Brady, Bethany L. Ash, and W. Robert Midden. 2020. “Scientific Investigations Report.”
Friedman, Michael S., Chad M. Rigsby, and Don Cipollini. 2020. “Light Limitation Impacts Growth but Not Constitutive or Jasmonate Induced Defenses Relevant to Emerald Ash Borer (Agrilus Planipennis) in White Fringetree (Chionanthus Virginicus) or Black Ash (Fraxinus Nigra).” Journal of Chemical Ecology 46 (11–12): 1117–30. https://doi.org/10.1007/s10886-020-01223-0.
Gabriel, J.L., and M. Quemada. 2011. “Replacing Bare Fallow with Cover Crops in a Maize Cropping System: Yield, N Uptake and Fertiliser Fate.” European Journal of Agronomy 34 (3): 133–43. https://doi.org/10.1016/j.eja.2010.11.006.
Gaspar, Adam. 2019. “Soil Fertility - Base Saturation and Cation Exchange Capacity.” December 17, 2019. https://www.pioneer.com/us/agronomy/Base-Saturation-Cation-Exchange-Capacity.html.
Gougoulias, Christos, Joanna M Clark, and Liz J Shaw. 2014. “The Role of Soil Microbes in the Global Carbon Cycle: Tracking the below-Ground Microbial Processing of Plant-Derived Carbon for Manipulating Carbon Dynamics in Agricultural Systems.” Journal of the Science of Food and Agriculture 94 (12): 2362–71. https://doi.org/10.1002/jsfa.6577.
Greenfield, Lucy M., Bahar S. Razavi, Nataliya Bilyera, Xuechen Zhang, and Davey L. Jones. 2021. “Root Hairs and Protein Addition to Soil Promote Leucine Aminopeptidase Activity of Hordeum Vulgare L.” Rhizosphere 18 (June): 100329. https://doi.org/10.1016/j.rhisph.2021.100329.
Hallama, Moritz, Carola Pekrun, Hans Lambers, and Ellen Kandeler. 2019. “Hidden Miners – the Roles of Cover Crops and Soil Microorganisms in Phosphorus Cycling through Agroecosystems.” Plant and Soil 434 (1): 7–45. https://doi.org/10.1007/s11104-018-3810-7.
Hao, X. J., T. Q. Zhang, C. S. Tan, T. Welacky, Y. T. Wang, D. Lawrence, and J. P. Hong. 2015. “Crop Yield and Phosphorus Uptake as Affected by Phosphorus-Based Swine Manure Application under Long-Term Corn-Soybean Rotation.” Nutrient Cycling in Agroecosystems 103 (2): 217–28. https://doi.org/10.1007/s10705-015-9735-0.
Hellerstein, Dan, Dennis Vilorio, and Marc Ribaudo. 2019. “Agricultural Resources and Environmental Indicators, 2019.” USDA ERS, 142.
Hou, Xianqing, Xiaojuan Wang, Rong Li, Zhikuan Jia, Lianyou Liang, Junpeng Wang, Junfeng Nie, Xin Chen, and Zhen Wang. 2012. “Effects of Different Manure Application Rates on Soil Properties, Nutrient Use, and Crop Yield during Dryland Maize Farming.” Soil Research 50 (6): 507–14. https://doi.org/10.1071/SR11339.
Hull & Associates. 2018. “Final Project Summary Report,” 214.
Hungria, Mariangela, Julio Cezar Franchini, Osvaldino Brandão-Junior, Glaciela Kaschuk, and Rosinei Aparecida Souza. 2009. “Soil Microbial Activity and Crop Sustainability in a Long-Term Experiment with Three Soil-Tillage and Two Crop-Rotation Systems.” Applied Soil Ecology 42 (3): 288–96. https://doi.org/10.1016/j.apsoil.2009.05.005.
Iqbal, Shazia, Umair Riaz, Ghulam Murtaza, Moazzam Jamil, Maqshoof Ahmed, Azhar Hussain, and Zafar Abbas. 2021. “Chemical Fertilizers, Formulation, and Their Influence on Soil Health.” In Microbiota and Biofertilizers: A Sustainable Continuum for Plant and Soil Health, edited by Khalid Rehman Hakeem, Gowhar Hamid Dar, Mohammad Aneesul Mehmood, and Rouf Ahmad Bhat, 1–15. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-48771-3_1.
Jamir, Esther, Rajiv Das Kangabam, Kasmika Borah, Abhijit Tamuly, H. P. Deka Boruah, and Yumnam Silla. 2019. “Role of Soil Microbiome and Enzyme Activities in Plant Growth Nutrition and Ecological Restoration of Soil Health.” In Microbes and Enzymes in Soil Health and Bioremediation, edited by Ashok Kumar and Swati Sharma, 99–132. Microorganisms for Sustainability. Singapore: Springer. https://doi.org/10.1007/978-981-13-9117-0_5.
Jayasundara, Susantha, Claudia Wagner-Riddle, Gary Parkin, Peter von Bertoldi, Jon Warland, Bev Kay, and Paul Voroney. 2007. “Minimizing Nitrogen Losses from a Corn–Soybean–Winter Wheat Rotation with Best Management Practices.” Nutrient Cycling in Agroecosystems 79 (2): 141–59. https://doi.org/10.1007/s10705-007-9103-9.
Jian, Jinshi, Brandon J. Lester, Xuan Du, Mark S. Reiter, and Ryan D. Stewart. 2020. “A Calculator to Quantify Cover Crop Effects on Soil Health and Productivity.” Soil and Tillage Research 199 (May): 104575. https://doi.org/10.1016/j.still.2020.104575.
Jiang, P., and K. D. Thelen. 2004. “Effect of Soil and Topographic Properties on Crop Yield in a North-Central Corn–Soybean Cropping System.” Agronomy Journal 96 (1): 252–58. https://doi.org/10.2134/agronj2004.0252.
Jin, Hongyan. 2010. “Characterization of Microbial Life Colonizing Biochar And Biochar-Amended Soils.” https://ecommons.cornell.edu/handle/1813/17077.
Julian, Ashley N., Louise Stevenson, and Megan A. Rúa. 2023. “Cover Crop Application on Dredged Sediments Increases Corn Yield through Microorganism-Associated Enzyme-Driven Nutrient Mineralization.” ver 1. https://portal.edirepository.org/nis/mapbrowse?packageid=edi.1404.1: Environment Data Initiative. https://doi.org/10.6073/pasta/6cf8f1903048b148e42deeae33478dd9.
Karlen, Douglas L., Cynthia A. Cambardella, John L. Kovar, and Thomas S. Colvin. 2013. “Soil Quality Response to Long-Term Tillage and Crop Rotation Practices.” Soil and Tillage Research 133 (October): 54–64. https://doi.org/10.1016/j.still.2013.05.013.
Kiani, Mina, Henn Raave, Asko Simojoki, Olga Tammeorg, and Priit Tammeorg. 2021. “Recycling Lake Sediment to Agriculture: Effects on Plant Growth, Nutrient Availability, and Leaching.” Science of The Total Environment 753 (January): 141984. https://doi.org/10.1016/j.scitotenv.2020.141984.
Kim, Nakian, María C. Zabaloy, Kaiyu Guan, and María B. Villamil. 2020. “Do Cover Crops Benefit Soil Microbiome? A Meta-Analysis of Current Research.” Soil Biology and Biochemistry 142 (March): 107701. https://doi.org/10.1016/j.soilbio.2019.107701.
Koudahe, Komlan, Samuel Allen, and Koffi Djaman. 2022. “Critical Review of the Impact of Cover Crops on Soil Properties | Elsevier Enhanced Reader.” 2022. https://doi.org/10.1016/j.iswcr.2022.03.003.
Lal, R., T.J. Logan, and N.R. Fausey. 1989. “Long-Term Tillage and Wheel Traffic Effects on a Poorly Drained Mollic Ochraqualf in Northwest Ohio. 1. Soil Physical Properties, Root Distribution and Grain Yield of Corn and Soybean.” Soil and Tillage Research 14 (4): 341–58. https://doi.org/10.1016/0167-1987(89)90054-8.
Lehmann, Johannes, Matthias C. Rillig, Janice Thies, Caroline A. Masiello, William C. Hockaday, and David Crowley. 2011. “Biochar Effects on Soil Biota – A Review.” Soil Biology and Biochemistry, 19th International Symposium on Environmental Biogeochemistry, 43 (9): 1812–36. https://doi.org/10.1016/j.soilbio.2011.04.022.
Lenth, Russell V., Paul Buerkner, Maxime Herve, Jonathon Love, Fernando Miguez, Hannes Riebl, and Henrik Singmann. 2022. “Emmeans: Estimated Marginal Means, Aka Least-Squares Means.” https://CRAN.R-project.org/package=emmeans.
Liebig, M. A., G. E. Varvel, J. W. Doran, and B. J. Wienhold. 2002. “Crop Sequence and Nitrogen Fertilization Effects on Soil Properties in the Western Corn Belt.” Soil Science Society of America Journal 66 (2): 596–601. https://doi.org/10.2136/sssaj2002.5960.
Liu, Defu, Zhenyue Feng, Hongde Zhu, Lihe Yu, Kejun Yang, Song Yu, Yifei Zhang, and Wei Guo. 2020. “Effects of Corn Straw Biochar Application on Soybean Growth and Alkaline Soil Properties.” BioResources 15 (1): 1463–81.
Mahama, G. Y., V. Prasad, K. L. Roozeboom, J. B. Nippert, and C. W. Rice. 2016. “Response of Maize to Cover Crops, Fertilizer Nitrogen Rates, and Economic Return | Agronomy Journal.” Agronomy Journal 108 (1): 17–31. https://doi.org/10.2134/agronj15.0136.
Marlin, John C. 2002. “Evaluation of Sediment Removal Options and Beneficial Use of Dredged Material for Illinois River Restoration: Preliminary Report,” June. https://www.ideals.illinois.edu/handle/2142/107173.
McDaniel, M.D., A.S. Grandy, L.K. Tiemann, and M.N. Weintraub. 2014. “Crop Rotation Complexity Regulates the Decomposition of High and Low Quality Residues.” Soil Biology and Biochemistry 78 (November): 243–54. https://doi.org/10.1016/j.soilbio.2014.07.027.
Mori, Taiki, Christoph Rosinger, and Andrew J. Margenot. 2023. “Enzymatic C:N:P Stoichiometry: Questionable Assumptions and Inconsistencies to Infer Soil Microbial Nutrient Limitation.” Geoderma 429 (January): 116242. https://doi.org/10.1016/j.geoderma.2022.116242.
Nafziger, Emerson. 2017. “Chapter 2: Corn.” Illinios Agronomy Handbook, 14.
NASS USDA. 2020. “Crop Production 2019 Summary 01/10/2020.” Crop Production, 124.
Nevens, F., and D. Reheul. 2001. “Crop Rotation versus Monoculture; Yield, N Yield and Ear Fraction of Silage Maize at Different Levels of Mineral N Fertilization.” NJAS - Wageningen Journal of Life Sciences 49 (4): 405–25. https://doi.org/10.1016/S1573-5214(01)80026-9.
NRCS USDA. 2020a. “Cover Crops - Keeping Soil in Place While Providing Other Benefits | NRCS New York.” 2020. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/ny/technical/?cid=nrcs144p2_027252.
NRCS USDA. 2020b. “Soil Health | NRCS Soils.” 2020. https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/health/.
Nyiraneza, J., M. H. Chantigny, A. N’Dayegamiye, and M. R. Laverdière. 2010. “Long-Term Manure Application and Forages Reduce Nitrogen Fertilizer Requirements of Silage Corn-Cereal Cropping Systems.” Agronomy Journal 102 (4): 1244–51. https://doi.org/10.2134/agronj2009.0480.
Oksanen, Jari, Gavin L. Simpson, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre, Peter R. Minchin, R. B. O’Hara, et al. 2022. “Vegan: Community Ecology Package.” https://CRAN.R-project.org/package=vegan.
Oliveira, Bruna R. F., Krisjan van Laarhoven, Martijn P. J. Smit, Huub H. M. Rijnaarts, and Tim Grotenhuis. 2017. “Impact of Compost and Manure on the Ripening of Dredged Sediments.” Journal of Soils and Sediments 17 (2): 567–77. https://doi.org/10.1007/s11368-016-1571-6.
Oliveira, Bruna R. F., Martijn P. J. Smit, Leon A. van Paassen, Tim C. Grotenhuis, and Huub H. M. Rijnaarts. 2017. “Functional Properties of Soils Formed from Biochemical Ripening of Dredged Sediments—Subsidence Mitigation in Delta Areas.” Journal of Soils and Sediments 17 (1): 286–98. https://doi.org/10.1007/s11368-016-1570-7.
Onufrak, Aaron, Megan Rúa, and Katie Hossler. 2019. “The Missing Metric: An Evaluation of Fungal Importance in Wetland Assessments,” 14. https://doi.org/10.1007/s13157-019-01228-w.
Ozlu, Ekrem, and Sandeep Kumar. 2018. “Response of Soil Organic Carbon, PH, Electrical Conductivity, and Water Stable Aggregates to Long-Term Annual Manure and Inorganic Fertilizer.” Soil Science Society of America Journal 82 (5): 1243–51. https://doi.org/10.2136/sssaj2018.02.0082.
Ozlu, Ekrem, Saroop S. Sandhu, Sandeep Kumar, and Francisco J. Arriaga. 2019. “Soil Health Indicators Impacted by Long-Term Cattle Manure and Inorganic Fertilizer Application in a Corn-Soybean Rotation of South Dakota.” Scientific Reports 9 (1): 11776. https://doi.org/10.1038/s41598-019-48207-z.
Pinheiro, José, Douglas Bates, Saikat DebRoy, Deepayan Sarkar, EISPACK, Siem Heisterkamp (Author fixed sigma), Bert Van Willigen (Programmer fixed sigma), Johannes Ranke (varConstProp()), and R-core. 2021. “Nlme: Linear and Nonlinear Mixed Effects Models.” https://CRAN.R-project.org/package=nlme.
Poorter, Hendrik, Karl J. Niklas, Peter B. Reich, Jacek Oleksyn, Pieter Poot, and Liesje Mommer. 2012. “Biomass Allocation to Leaves, Stems and Roots: Meta-Analyses of Interspecific Variation and Environmental Control.” New Phytologist 193 (1): 30–50. https://doi.org/10.1111/j.1469-8137.2011.03952.x.
Postma, Johannes Auke, Annette Dathe, and Jonathan Paul Lynch. 2014. “The Optimal Lateral Root Branching Density for Maize Depends on Nitrogen and Phosphorus Availability.” Plant Physiology 166 (2): 590–602. https://doi.org/10.1104/pp.113.233916.
Raut, Yogendra Y., Vinayak S. Shedekar, Khandakar R. Islam, Javier M. Gonzalez, Dexter B. Watts, Warren A. Dick, Dennis C. Flanagan, et al. 2020. “Soybean Yield Response to Gypsum Soil Amendment, Cover Crop, and Rotation.” Agricultural & Environmental Letters 5 (1): e20020. https://doi.org/10.1002/ael2.20020.
Rehm, George. 1994. “Soil Cation Ratios for Crop Production.” North Central Regional Extension Publication 533: 6.
Rengasamy, Pichu, Claudivan Feitosa de Lacerda, and Hans Raj Gheyi. 2022. “Salinity, Sodicity and Alkalinity.” In Subsoil Constraints for Crop Production, edited by Teogenes Senna de Oliveira and Richard Willian Bell, 83–107. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-00317-2_4.
Richardson, Jesse J., and Elizabeth Dooley. 2017. “United States Soil Degradation.” In International Yearbook of Soil Law and Policy 2016, edited by Harald Ginzky, Irene L. Heuser, Tianbao Qin, Oliver C. Ruppel, and Patrick Wegerdt, 411–30. International Yearbook of Soil Law and Policy. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-42508-5_24.
Rúa, Megan A., Ashley N. Julian, and Louise Stevenson. 2023. “Dredged Sediments Contain Potentially Beneficial Microorganisms for Agriculture and Little Harmful Cyanobacteria.” Journal of Sustainable Agriculture and Environment n/a (n/a). https://doi.org/10.1002/sae2.12037.
Russell, A. E., D. A. Laird, and A. P. Mallarino. 2006. “Nitrogen Fertilization and Cropping System Impacts on Soil Quality in Midwestern Mollisols.” Soil Science Society of America Journal 70 (1): 249–55. https://doi.org/10.2136/sssaj2005.0058.
Sánchez de Cima, Diego, Berit Tein, Vyacheslav Eremeev, Anne Luik, Karin Kauer, Endla Reintam, and Greete Kahu. 2016. “Winter Cover Crop Effects on Soil Structural Stability and Microbiological Activity in Organic Farming.” Biological Agriculture & Horticulture 32 (3): 170–81. https://doi.org/10.1080/01448765.2015.1130646.
Scherr, Sara, and Satya Yadav. 2020. “Land Degradation in the Developing World: Implications for Food, Agriculture, and the Environment to 2020.” Food, Agriculture, and the Environment Discussion Paper, International Food Policy Research Institute, 14: 42.
Schipanski, Meagan E., Mary Barbercheck, Margaret R. Douglas, Denise M. Finney, Kristin Haider, Jason P. Kaye, Armen R. Kemanian, et al. 2014. “A Framework for Evaluating Ecosystem Services Provided by Cover Crops in Agroecosystems.” Agricultural Systems 125 (March): 12–22. https://doi.org/10.1016/j.agsy.2013.11.004.
Sigua, Gilbert. 2005. “Current and Future Outlook of Dredged and Sewage Sludge Materials in Agriculture and Environment (3 Pp).” Journal of Soils and Sediments 5 (1): 50–52. https://doi.org/10.1065/jss2004.10.113.
Singer, J. W., K. A. Kohler, M. Liebman, T. L. Richard, C. A. Cambardella, and D. D. Buhler. 2004. “Tillage and Compost Affect Yield of Corn, Soybean, and Wheat and Soil Fertility.” Agronomy Journal 96 (2): 531–37. https://doi.org/10.2134/agronj2004.5310.
Sinsabaugh, R.L, H Reynolds, and T.M Long. 2000. “Rapid Assay for Amidohydrolase (Urease) Activity in Environmental Samples.” Soil Biology and Biochemistry 32 (14): 2095–97. https://doi.org/10.1016/S0038-0717(00)00102-4.
Song, Dali, Xiangyin Xi, Qin Zheng, Guoqing Liang, Wei Zhou, and Xiubin Wang. 2019. “Soil Nutrient and Microbial Activity Responses to Two Years after Maize Straw Biochar Application in a Calcareous Soil.” Ecotoxicology and Environmental Safety 180 (September): 348–56. https://doi.org/10.1016/j.ecoenv.2019.04.073.
Srour, Ali Y., Hala A. Ammar, Arjun Subedi, Mirian Pimentel, Rachel L. Cook, Jason Bond, and Ahmad M. Fakhoury. 2020. “Microbial Communities Associated With Long-Term Tillage and Fertility Treatments in a Corn-Soybean Cropping System.” Frontiers in Microbiology 11. https://doi.org/10.3389/fmicb.2020.01363.
Steele, M.K., F.J. Coale, and R.L. Hill. 2012. “Winter Annual Cover Crop Impacts on No-Till Soil Physical Properties and Organic Matter.” Soil Science Society of America Journal 76 (6): 2164. https://doi.org/10.2136/sssaj2012.0008.
Steven Wallander, David Smith, Maria Bowman, and Roger Claassen. 2021. “Cover Crop Trends, Programs, and Practices in the United States.” Economic Information Bulletin (USDA ERS) 222 (February): 33.
Tarkalson, David D., Gary W. Hergert, and Kenneth G. Cassman. 2006. “Long-Term Effects of Tillage on Soil Chemical Properties and Grain Yields of a Dryland Winter Wheat–Sorghum/Corn–Fallow Rotation in the Great Plains.” Agronomy Journal 98 (1): 26–33. https://doi.org/10.2134/agronj2004.0240.
Thaler, Evan A., Isaac J. Larsen, and Qian Yu. 2021. “The Extent of Soil Loss across the US Corn Belt.” Proceedings of the National Academy of Sciences 118 (8): e1922375118. https://doi.org/10.1073/pnas.1922375118.
Tian, Guanglong, Chih-Yu Chiu, Alan J. Franzluebbers, Olawale O. Oladeji, Thomas C. Granato, and Albert E. Cox. 2015. “Biosolids Amendment Dramatically Increases Sequestration of Crop Residue-Carbon in Agricultural Soils in Western Illinois.” Applied Soil Ecology 85 (January): 86–93. https://doi.org/10.1016/j.apsoil.2014.09.001.
Toledo Weather Records. 2019. Accessed December 8, 2022. https://www.extremeweatherwatch.com/cities/toledo.
US EPA, OW. 2013. “The Sources and Solutions: Agriculture.” Overviews and Factsheets. US EPA. March 12, 2013. https://www.epa.gov/nutrientpollution/sources-and-solutions-agriculture.
USDA, Office of Secretary. 2019. “USDA Disaster Designation July 2019.” https://governor.ohio.gov/media/news-and-media/07292019a.
Vermeulen, J., S.G. van Dijk, J.T.C. Grotenhuis, and W.H. Rulkens. 2005. “Quantification of Physical Properties of Dredged Sediments during Physical Ripening.” Geoderma 129 (3–4): 147–66. https://doi.org/10.1016/j.geoderma.2004.12.040.
Villamil, M. B., G. A. Bollero, R. G. Darmody, F. W. Simmons, and D. G. Bullock. 2006. “No-Till Corn/Soybean Systems Including Winter Cover Crops.” Soil Science Society of America Journal 70 (6): 1936. https://doi.org/10.2136/sssaj2005.0350.
Vu, Vince Q. 2011. “Ggbiplot: A Ggplot2 Based Biplot.” R package 342.
Wallander, Steven. 2013. “USDA ERS - While Crop Rotations Are Common, Cover Crops Remain Rare.” 2013. https://www.ers.usda.gov/amber-waves/2013/march/while-crop-rotations-are-common-cover-crops-remain-rare/.
Whalen, Joann K., Quancai Hu, and Aiguo Liu. 2003. “Compost Applications Increase Water-Stable Aggregates in Conventional and No-Tillage Systems.” Soil Science Society of America Journal 67 (6): 1842–47. https://doi.org/10.2136/sssaj2003.1842.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, Dewey Dunnington, and RStudio. 2022. “Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics.” https://CRAN.R-project.org/package=ggplot2.
Wittwer, Raphaël A., and Marcel G. A van der Heijden. 2020. “Cover Crops as a Tool to Reduce Reliance on Intensive Tillage and Nitrogen Fertilization in Conventional Arable Cropping Systems.” Field Crops Research 249 (April): 107736. https://doi.org/10.1016/j.fcr.2020.107736.
Woods, Michaela J., Elizabeth Roberson, Don Cipollini, and Megan A. Rúa. 2019. “White-Tailed Deer and an Invasive Shrub Facilitate Faster Carbon Cycling in a Forest Ecosystem.” Forest Ecology and Management 448 (September): 104–11. https://doi.org/10.1016/j.foreco.2019.05.068.
Zhu, Jing, Min Li, and Mick Whelan. 2018. “Phosphorus Activators Contribute to Legacy Phosphorus Availability in Agricultural Soils: A Review.” Science of The Total Environment 612 (January): 522–37. https://doi.org/10.1016/j.scitotenv.2017.08.095.
Zhu, Shusheng, Jorge M. Vivanco, and Daniel K. Manter. 2016. “Nitrogen Fertilizer Rate Affects Root Exudation, the Rhizosphere Microbiome and Nitrogen-Use-Efficiency of Maize.” Applied Soil Ecology 107 (November): 324–33. https://doi.org/10.1016/j.apsoil.2016.07.009.

JulianetalPlantSoilsupplemental.docx

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Cover crop application on dredged sediments increases corn yield through microorganism-associated enzyme-driven nutrient mineralization.

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Abstract

Figures

Introduction

Methods

Site Description

Plot Preparation and Planting

Soil Sample Collection

Soil Physical and Chemical Properties

Bulk Soil Enzyme Activity

Plant Responses

Statistical Analysis

Results

Soil physiochemical properties

Enzyme Activities

Plant Responses

Soil properties and plant responses

Discussion

Nutrient Dynamics Drive Corn Biomass and Production

Cover Crop Application Alone Drives Corn Yield Increases

Soil Properties Differ in Response to Cover Crop Application

Conclusion

Declarations

References

Supplementary Files

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