Volume 7, Issue 2 e20088

RESEARCH LETTER

Open Access

Assessing how cover crops close the soil health gap in on-farm experiments

Fernanda Souza Krupek,

Corresponding Author

Fernanda Souza Krupek

[email protected]

orcid.org/0000-0002-6132-2770

Dep. of Agronomy and Horticulture, Univ. of Nebraska–Lincoln, 1875 N. 38th St., Lincoln, NE, 68583 USA

Correspondence

Fernanda Souza Krupek, Dep. of Agronomy and Horticulture, Univ. of Nebraska–Lincoln, 1875 N. 38th St., Lincoln, NE 68583, USA.

Email: [email protected]

Contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review & editing

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Steven Mugisha Mizero,

Steven Mugisha Mizero

School of Environmental Sciences, Univ. of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1 Canada

Contribution: Data curation, Formal analysis, Methodology, Writing - review & editing

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Daren Redfearn,

Daren Redfearn

orcid.org/0000-0003-0295-3131

Dep. of Agronomy and Horticulture, Univ. of Nebraska–Lincoln, 1875 N. 38th St., Lincoln, NE, 68583 USA

Contribution: Funding acquisition, Supervision, Writing - review & editing

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Andrea Basche,

Andrea Basche

orcid.org/0000-0001-6805-8522

Dep. of Agronomy and Horticulture, Univ. of Nebraska–Lincoln, 1875 N. 38th St., Lincoln, NE, 68583 USA

Contribution: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing - review & editing

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Fernanda Souza Krupek,

Corresponding Author

Fernanda Souza Krupek

[email protected]

orcid.org/0000-0002-6132-2770

Dep. of Agronomy and Horticulture, Univ. of Nebraska–Lincoln, 1875 N. 38th St., Lincoln, NE, 68583 USA

Correspondence

Fernanda Souza Krupek, Dep. of Agronomy and Horticulture, Univ. of Nebraska–Lincoln, 1875 N. 38th St., Lincoln, NE 68583, USA.

Email: [email protected]

Contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review & editing

Search for more papers by this author

Steven Mugisha Mizero,

Steven Mugisha Mizero

School of Environmental Sciences, Univ. of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1 Canada

Contribution: Data curation, Formal analysis, Methodology, Writing - review & editing

Search for more papers by this author

Daren Redfearn,

Daren Redfearn

orcid.org/0000-0003-0295-3131

Dep. of Agronomy and Horticulture, Univ. of Nebraska–Lincoln, 1875 N. 38th St., Lincoln, NE, 68583 USA

Contribution: Funding acquisition, Supervision, Writing - review & editing

Search for more papers by this author

Andrea Basche,

Andrea Basche

orcid.org/0000-0001-6805-8522

Dep. of Agronomy and Horticulture, Univ. of Nebraska–Lincoln, 1875 N. 38th St., Lincoln, NE, 68583 USA

Contribution: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing - review & editing

Search for more papers by this author

First published: 24 August 2022

https://doi.org/10.1002/ael2.20088

Citations: 5

Assigned to Associate Editor Sindhu Jagadamma.

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Abstract

Assessing the success of cover crops (CCs) as a way to promote soil health at the farm scale remains a challenge. At four on-farm CC experiments in Nebraska, we quantified soil health relative to a reference soil. We examined physical, chemical, and biological properties in near-surface soil. Cover crops reduced the soil health gap between bare (no-CC) and reference soil in the short (3-yr) timescale, but the magnitude of responses depended on cropland management history and ecological dynamics of reference site plant communities. Increases in soil health relative to reference soils showed some relationship to increases in soybean [Glycine max (L.) Merr.] and corn (Zea mays L.) yields. Clear discrimination of reference from bare soils was most influenced by organic matter and infiltration measurements conducted under the highest sampling intensity. Framing soil metrics relative to reference soils and ensuring appropriate sampling intensity are important to quantify the effects of CC on farm landscapes.

Core Ideas

We used a soil health gap framework with reference soils to assess cover crop impacts at the farm scale.
Cover crops reduced the soil health gap between cropland and reference soil in less than three years.
Soil health relative to a reference soil was weakly associated with grain yields.
Reference soils embodying soil health principles can be used to evaluate cover crops success on farms.
Sampling intensity needs to be sufficiently high to accurately represent cover impacts at the farm scale.

Abbreviations

CC: cover crop
no-CC: control no cover crop
RSH: relative soil health

1 INTRODUCTION

Cover crops (CCs) are promoted as a strategy for changing soil properties that lead to improved soil function such as water infiltration, carbon sequestration, nutrient retention, erosion control, and belowground biodiversity. Examples from long-term research (DeLaune et al., 2019; Mbuthia et al., 2015), literature synthesis (Blanco-Canqui et al., 2015; Schipanski et al., 2014), and on-farm trials (Welch et al., 2016; Wood & Bowman, 2021) have documented soil property improvements (e.g., organic matter, water infiltration, β-glucosidase activity, aggregation) with CCs. Yet it remains difficult to detect CC-related changes on soil, particularly in on-farm studies where large experimental plots include greater inherent variability. In this analysis, we incorporated two approaches to refine the assessment of CC impacts on soil properties: reference soils and sampling intensity strategies.

Soil property changes relative to reference soils have been applied to understand realistically attainable improvements resulting from management changes (Dobarco et al., 2021; Maharjan & Das, 2021). The concept of reference state was first introduced in ecological sites to understand rangeland health (Caudle et al., 2013; Pellant et al., 2005) and represents undisturbed soils with a minimum degree of anthropogenic modification (Dobarco et al., 2021). Advancements on the concept of reference state are being applied in dynamic soil properties (Wills et al., 2017), temporal evolution of soil properties for thresholds and stages of degradation (Kuzyakov & Zamanian, 2019), mapping soil classes and land use for reference state identification (Dobarco et al., 2021; Maharjan & Das, 2021), and the soil health gap concept (Maharjan et al., 2020). However, much less is known about the application of reference soils on on-farm assessments of soil changes with CCs.

Assessing changes in soil properties with CCs at the farm scale is potentially also influenced by sampling strategy. In a meta-analysis of 86 studies in North America, Stewart et al. (2018) found that water infiltration had high responsiveness to 1–3 yr of CC adoption, being a key parameter to compare cropland soil health to reference soils. Similar to other properties, water infiltration is predominantly dependent on the spatial and temporal scale of assessment (de Lima Moraes et al., 2020). As a result, spatial and temporal replication of measurements is usually required for adequate soil hydrologic characterization (Reynolds et al., 2002).

In this study, we quantified soil property changes relative to reference soils in on-farm CC trials. Specific objectives were (a) to apply the concept and assess whether soil health gap relative to reference soils has significant relationship with crop yield and (b) to understand soil property combinations and sampling strategies that maximally distinguish soils with a perenniality gradient (reference, CC, and no-CC).

2 MATERIALS AND METHODS

2.1 On-farm experiments

Four on-farm trials were part of the Nebraska Soil Health Initiative between 2016 and 2021. Soils were classified predominantly as Mollisols, Entisols, and Alfisols (Krupek et al., 2022), with slope gradients ranging from 1 to 23% (Soil Survey Staff, 1999). Trials were randomized complete block designs with treatments of multispecies CC and no-CC across approximately 30-ha fields located in Greeley, Howard, Merrick, and Colfax counties. In a corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation, farmers introduced diverse CC mixtures of cool-season small grain cereals, legumes, brassicas, and warm-season summer annual grasses based on USDA-NRCS CC guidelines (Nebraska Extension On-Farm Research, 2021; USDA-NRCS, 2011). Trials required farmer-reported grain yield data collected using a test plot weigh wagon (Greeley) or combine with a calibrated yield monitor (Howard, Merrick, and Colfax).

Core Ideas

We used a soil health gap framework with reference soils to assess cover crop impacts at the farm scale.
Cover crops reduced the soil health gap between cropland and reference soil in less than three years.
Soil health relative to a reference soil was weakly associated with grain yields.
Reference soils embodying soil health principles can be used to evaluate cover crops success on farms.
Sampling intensity needs to be sufficiently high to accurately represent cover impacts at the farm scale.

2.2 Soil sampling

Samples were collected in July 2019, 3 yr after the implementation of the first CC treatment, when plots were around soybean growth stage R6. We sampled 0-to-5-cm depth soils using a 32-mm core diameter sampler. Minimally disturbed soils (i.e., sites representing perennial grassland or less-disturbed land uses) from nearby (<5 km) farmland were collected to determine soil health benchmarks (Maharjan et al., 2020). Selection of reference sites accounted for soil and climate variabilities within on-farm trials. Each reference site had functional Ecological Site Description vegetation with major components of the historical climax plant community. Based on state and transition models (Bestelmeyer et al., 2017), field assessments showed vegetation communities in Native/Invaded Grass State 2.1, Switchgrass/Prairie Sand Reed Plant Community 1.2, Native/Invaded Mix State Community 2.2–Codominant, and Native/Invaded Grass State 2.1 for reference soils in Greeley, Merrick, Colfax, and Howard respectively.

To minimize spatial soil variation, in each sample point, 10 cores were collected around a 6-m × 24-m sample area, composited, and shipped on ice to Ward Laboratories (Kearney, NE). Soil property analysis was conducted using standard methods for soil organic matter (Nelson & Sommers, 1996), nitrate-nitrogen (Keeney & Nelson, 1982), β-glucosidase activity (Tabatabai, 1994), exchangeable bases (Thomas, 1983), and total elemental iron and manganese. One intact soil core was collected per sample area to determine bulk density by the core method (Blake & Hartge, 1986). Wet aggregate stability, expressed as mean weight diameter of water-stable aggregates, was measured by wet sieving method (Nimmo & Perkins, 2002) using a modified Yoder wet-sieving device (Yoder, 1936) from the University of Nebraska–Lincoln.

Multiple measurements of initial water infiltration (Smith, 1999) were conducted in 2019 and 2021 to assess sampling intensity strategies that maximally distinguish soil managements. In the 6-m × 24-m sample area, we performed two (N = 2), three (N = 3), four (N = 4), and five (N = 5) consecutive measurements of water infiltration (Figure 1a), representing a gradual increase in sampling intensity.

Details are in the caption following the image — **FIGURE 1**
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(a) Layout of the area within each sampling point for field measurement and multiple soil initial water infiltration measurements of increased sampling intensity (two [N = 2], three [N = 3], four [N = 4], and five [N = 5] consecutive measurements of water infiltration). (b) Canonical discriminant analysis of initial water infiltration measurements, cation exchange capacity (CEC), mean weight diameter of water-stable aggregates (MDW), and organic matter of reference, cover cropping and no cover cropping with increasing sampling intensity. Average of N = 2, N = 3, N = 4, and N = 5 consecutive measurements of water infiltration per sampling location. The scores along the first two axes are shown. Reference, cover cropping, and no cover cropping are indicated with square, circle, and triangle, respectively. Field clusters are indicated by ovals with dots representing the group centroid for each treatment in a 95% confidence ellipse. Arrows indicate the relative importance (length) and correlation (angle with axis) between each variable retained in the model and the first (Can 1) and second (Can 2) canonical axes. Differences between treatments (represented by distance between ellipses) increase with greater sampling intensity

2.3 Data analysis

To describe cropland soil function compared with its local native potential, we calculated a relative soil health index (RSH) as suggested by Williams et al. (2020). For each on-farm site (i) soil property values for the CC and no-CC (f) were divided by the respective value for the reference soil (r).

\begin{equation}{\rm{RSH}} = \frac{{{\rm{Soil\, property\, value}}_{i{\rm{f}}}}}{{{\rm{Soil\, property\, value}}_{i{\rm{r}}}}}\end{equation}

(1)

The RSH concept was applied to properties that follow “more-is-better” and “less-is-better” patterns according to scoring functions of the Cornell comprehensive assessment of soil health (Moebius-Clune et al., 2016). Higher infiltration, aggregate stability, β-glucosidase, organic matter, nitrate, and cation exchange capacity values relative to reference soils indicated improved soil functioning as far as efficient filtration, erosion control, belowground biodiversity, carbon sequestration, and nutrient retention. Likewise, lower values of bulk density, manganese, and iron relative to reference soils were associated with better soil functioning as far as reduced soil compaction and risks associated with micronutrient toxicity.

Yield data were post-processed using Yield Editor (USDA-ARS, 2021) and adjusted to 13% (soybean) and 15.5% (maize) moisture content. Yield data were co-localized from soil sample area (average of 20–30 data points from yield monitoring systems) for the Colfax, Howard, and Merrick sites and averaged across strips for the Greeley site.

We fit an analysis of variance model for RSH with block and treatment as fixed effects using aov function in R version 4.0.4 (R Core Team, 2020). Mean separation comparisons were performed using lsmeans package at a p value of .10 (Lenth, 2016). We conducted simple linear regressions in lme4 package to understand the relationship between RSH and yield (Bates et al., 2015). Canonical Discriminant Analysis (CDA) was performed using candisc package (Friendly, 2007) to provide insights into appropriate sampling strategy and associations among soil properties to better differentiate management groups.

3 RESULTS AND DISCUSSION

3.1 Reference state comparisons determine how CCs close the soil health gap

For more-is-better properties, CCs had higher cumulative RSH values at two sites and at least one property with a higher RSH value at the other two sites (Figure 2a). We observed differences in the magnitude of responses; CCs reduced the soil health gap by 55, 28, 17, and 14% in Greeley, Colfax, Howard and Merrick sites, respectively (Figure 2a). For less-is-better properties, CC had lower RSH values than no-CC in three out of the four sites, reducing the soil health gap by 67, 47, and 12% in Colfax, Howard, and Greeley sites, respectively (Figure 2b). Across sites and soil properties, Colfax had the greatest magnitude and significant responses to CCs (Figure 2a andb), most likely because finer texture led to soil improvements through formation of water-stable aggregates and organic carbon accumulation (Tisdall & Oades, 1982). Most of the soil properties dynamically responded to CC, but responses were site-specific, as might be expected for soils from different ecological sites varying in management and vegetation (Figure 2a and b) (Wills et al., 2017). Overall, the RSH concept successfully captured soil function improvements through infiltration, aggregation, erosion control, belowground biodiversity, carbon sequestration, and nutrient retention, all of which were soil concerns identified by our farmers and reasons they were early adopters for experimentation with CC practices (Bowman et al., 2022).

The RSH concept can illustrate CC impacts and account for soil and climate variabilities. However, cropland management history and state of reference soils, constructed through state and transition models, should be considered (Wills et al., 2017). The highest RSH (more-is-better properties) at Howard and Greeley sites with CCs (Figure 2a) is likely the result of (a) greater CC biomass accumulation than expected for Nebraska agroecosystems (Ruis et al., 2020) at the Howard site (3.07 Mg ha⁻¹), (b) long-term (>10 yr) no-tillage in the Greeley site improving soil environment (Helgason et al., 2010; Jiang et al., 2011; Six et al., 2004), and (c) comparable vegetation ecological dynamics of reference soils in the Greeley and Howard sites (Native/Invaded Grass State 2.1) (Bestelmeyer et al., 2003; Stringham et al., 2003). Such interpretations can serve as a useful tool for soil resources management, bringing the whole ecosystem insight into management decisions.

In six of eight site-experiment years, farmers achieved higher yields when the cropland RSH value for infiltration rate was higher (Figure 2c and d). Even though the relationship was positive in most site-experiment years, overall the relationship of grain yield was only weakly associated with RSH and similar to those recently reported for soil health and grain quality (Adhikari et al., 2022). Chalise et al. (2018) similarly found that CC led to increases in soil water infiltration and soybean yield by 80 and 14%, respectively, compared with no-CC. Even though the interpretation of yield and soil health relationships are soil property and location specific, several studies have suggested that CC adoption can improve the soil fertility, structure, and hydrothermal properties while increasing cash crop yield stability (Crookston et al., 2021; Fontana et al., 2021; Williams et al., 2018). Although the relationship was not more definitive in our analysis, linking soil health relative to a reference state with yield outcomes is an active area of research that deserves more attention in the development of soil health assessments on farms (Wood & Blankinship, 2022).

3.2 The importance of sampling intensity when assessing the soil health gap with CC

There was a clear separation between reference vs. no-CC and CC vs. no-CC in the first canonical axis (Can1) axis under the highest (N = 5) sampling intensity (Figure 1b), indicating that five consecutive measurements of water infiltration captured the salient differences among management. Sampling intensity influences the field measurement representativeness and quality of soil dataset quality (Hartemink et al., 2008), which is relevant in sampling methods for digital soil mapping (Guo et al., 2018) and ecosystem restoration of degraded land (Chacoff et al., 2012). The CDA biplot arrows (Figure 1b) indicate the relative importance (length) and correlation (angle with axis) between each variable retained in the model and the canonical axes. Organic matter and water infiltration showed the greatest relative influence, which is represented by the size of the arrows in the CDA biplots (Figure 1b). This is likely the result of improved aggregation (Stumpf et al., 2016), greater fine root production (Sprunger et al., 2017), and enhanced microbial activity (Tiemann & Grandy, 2015) of reference soils representing greater perenniality (Sprunger et al., 2020).

Soil health indicators should be relevant, sensitive, and practical (Lehmann et al., 2020). As a measurement conducted inexpensively and with a short turnaround time, water infiltration showed the highest responsiveness to CC across all sites (Figure 2a) as reported previously in the literature (Alvarez et al., 2017; Basche & DeLonge, 2019; Stewart et al., 2018). In our study, water infiltration was repeated over space (increased sampling intensity) and time, whereas lab analysis samples were composited from cores collected within each sampling area. Even though collection of multiple samples (e.g., high resolution stratified sampling) for lab analysis could have improved the ability to detect soil property changes (Pennock et al., 2008), these results point to the high variability produced by field and laboratory assessments. This limits researchers’ ability to detect statistical significant effects of CC, even when improvements in soil properties or plant productivity are observed by farmers (Gutknecht et al., 2022; Smith, 2020). Together, these results suggest that positive effects from CC adoption on farms can be achieved by integrating a reference-based soil health concept and adequate sampling.

ACKNOWLEDGMENTS

We thank the farmers who are part of the Soil Health Initiative for making this work possible, with special thanks to J. Sack, T. Ingram, B. Hopkins, and N. Seim for managing the on-farm trial and allowing our research team to visit their fields for data collection. We also thank Aaron Hird, USDA-NRCS field staff, for help with communication with farmers and selection of the reference sites included in this work; and University of Nebraska–Lincoln Undergraduate Creative Activities and Research Experiences (UNL-UCARE) scholars for supporting field sampling. We also thank the three anonymous reviewers for the detailed, thoughtful, and supportive comments that helped improve and clarify this manuscript. Financial support for this research was provided by the Natural Resources Conservation Service (USDA-NRCS) under agreement no. 68-6526-17-005. Additional program funding support was provided by the Robert R. Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska–Lincoln.

AUTHOR CONTRIBUTIONS

Fernanda Souza Krupek: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Writing – original draft; Writing – review & editing. Steven Mugisha Mizero: Data curation; Formal analysis; Methodology; Writing – review & editing. Daren Redfearn: Funding acquisition; Supervision; Writing – review & editing. Andrea Basche: Conceptualization; Funding acquisition; Project administration; Resources; Supervision; Writing – review & editing.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

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Assessing how cover crops close the soil health gap in on-farm experiments

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