Next Article in Journal
Changes in Acidic Soil Chemical Properties and Carbon Dioxide Emission Due to Biochar and Lime Treatments
Previous Article in Journal
Application of Novel Microorganism-Based Formulations as Alternative to the Use of Iron Chelates in Strawberry Cultivation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 11
Issue 3
10.3390/agriculture11030218
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Quantifying Root-Soil Interactions in Cover Crop Systems: A Review

by
Cameron M. Ogilvie
,
Waqar Ashiq
,
Hiteshkumar B. Vasava
and
Asim Biswas
*
School of Environmental Sciences, University of Guelph, Guelph, ON N1G2W1, Canada
*
Author to whom correspondence should be addressed.
Agriculture 2021, 11(3), 218; https://doi.org/10.3390/agriculture11030218
Submission received: 28 January 2021 / Revised: 22 February 2021 / Accepted: 3 March 2021 / Published: 6 March 2021
Versions Notes

Abstract

:
Plant roots are an integral part of soil ecosystems and contribute to various services, including carbon and nutrient cycling, weathering, and soil formation. They also modify soil physical properties (e.g., soil water content, pore size distribution, and bulk density) and impact subsequent crops’ growth. Cover crops have been reported to improve soil and environmental quality by reducing nutrient losses, improving soil water content, and increasing soil organic matter. Understanding the complex interactions between cover crop roots and soil (RS) is of utmost importance. However, cover crop RS interactions have not been critically reviewed. In this article, we investigated the nature of cover crop physical RS interactions and explored the emerging technologies for their study. We also assessed technologies that may be readily applied to the study of physical RS interactions in cover crop systems and discussed ways to improve related research in the future.

1. Introduction

Roots are said to have succeeded rhizoid-based rooting systems in evolutionary history approximately 400 million years ago. This development caused plants to become increasingly involved in transforming terrestrial ecosystems by influencing carbon cycling and weathering of mineral parent materials [1]. Plant roots began to play a critical role in soil formation by re-shaping soil systems [2]. With this understanding, researchers have begun to redesign input-intensive agroecosystems to mimic natural ones where plants are valued, not only for what they produce for human consumption but also their ecosystem services [3]. Consequently, in recent decades, plants’ employment for ameliorating soil physical constraints to crop production via root-soil (RS) interactions has received significant research attention [4,5,6,7].
Cover crops are the crops grown in agroecosystems for ecological services (e.g., soil cover, nutrient capture, fertility improvement, weed suppression) rather than a harvestable product [8,9]. The use of cover crops within grain crop rotations for improving soil physical properties, i.e., reducing soil compaction, improving pore size distribution, and forming biopores, has been validated by several studies [10,11,12,13,14,15,16,17]. In tandem with this, emerging technologies are now allowing for new insights into physical RS interactions that were previously unobservable through destructive sampling [11,18]. Thus, the aim of this review is three-fold: to briefly investigate the nature of cover crop physical RS interactions, to explore the landscape of emerging technologies for study of physical RS interactions, and finally to assess which technologies may be most readily applied to the study of physical RS interactions in cover crop systems.

2. Root-Soil Interactions and the Role of Cover Crops

In agroecosystems, roots perform three basic functions for the plants: support, water supply, and nutrient supply. Root activity is intimately tied to the plant water and nutrient status, as well as internal carbohydrate supply, such that both root and shoot growth and development establish a functional equilibrium [19]. Roots also produce hormonal signals that rapidly regulate plant growth based on the rhizosphere condition [20]. For example, root meristematic cells integrate signals from the environment to cope with external constraints which requires the activation of hormone homeostasis and signaling pathways through transcriptional regulation.
Five processes govern root system morphology: emergence of new main axes, branching, axial growth (root elongation and direction), radial growth (thickening), and root senescence and decay [21]. These processes are internally regulated based on environmental conditions. Branching is primarily regulated by nutrient availability, with varied patterns depending on the type and nutrient availability [22]. Several tropisms regulate axial root growth. Gravitropism is commonly recognized as the dominant tropism in seminal roots but not lateral roots [22]. Recent studies in root growth have revealed that trematotropism—growth toward holes or voids—is differentially expressed across species [18,23]. Small cereals such as wheat (Triticum aestevum L.) or barley (Hordeum vulgare L.) tend to cross macropore channels. At the same time, corn (Zea mays L.) or soybeans (Glycine max L.) are more likely to colonize them and re-enter the bulk soil at lower depths which is explained by oxytropism.
Root morphology is also affected by soil water content. Bao et al. [24] demonstrated that seminal root’s contact with moist surfaces induces the auxin-regulated formation of lateral roots to explore the wet soil. This response was referred to as “hydropatterning”. Water and nutrient uptake are not homogenous across the root axis [25]; some sections exhibit larger water and nutrient uptake levels, while others appear to be dormant [21]. Oxygen deficits decrease the rate of root growth and cause root death, while soil water deficits also reduce root elongation. However, they can increase the rooting depth and density in some species [26]. For example, field pea (Pisum sativum L.) and chickpea (Cicer arientinum L.) both increased their rooting depth in response to water deficiency, while soybeans roots showed no impact [27]. A similar differential response may exist for cultivars of the same species [28].

2.1. Roots as Affected by Changes in Soil Physical Properties

Soil physical factors also constrain root growth. Hard soil reduces the root elongation process [26,29]. The contact with hard soil increases radial root growth, building up pressure and weakening the soil [29]. This mechanism is called “inverse peristalsis” [30]. On the other hand, loose soils do not allow optimal root growth [31]. The degree of response to soil compaction is regulated by plant genetics. For example, common vetch (Vicia sativa L.) roots marginally explored compact soil layers compared to tillage radish (Raphanus sativus L. cv. ‘Early Mino’) and black oats (Avena strigosa Schreb. cv. ‘Pratex’) [11].
Increasing soil porosity benefits crop root systems. Subsoil macropores formed by shrinking and swelling of the soil [32,33], and biopores formed by soil fauna and plant roots, can increase soil aeration, water infiltration, and root access deeper soil water and nutrients [31,34,35,36,37]. Colombi et al. [18] and Pfeifer et al. [23] observed colonization of artificial macropores by plant roots. However, the above-ground biomass was not affected. Root colonization of soil macropores was also reported to reduce water uptake due to poor RS contact [38].

2.2. Soil Physical Properties as Affected by Root Growth

Soil physical properties constrain root growth and are shaped by root growth [39]. As roots experience resistance from the soil, axial and radial growth creates pressure that rearranges soil particles and pore spaces. Bodner et al. [10] proposed that various root sizes assume different growth strategies. Fine roots exploit existing pore pathways, increasing local soil drying, and coalescing aggregates. Coarse roots pioneer new channels between soil aggregates and densify the surrounding rhizosphere soil [40]. Local soil drying caused by plant transpiration further enhances macroporosity since the increased frequency and amplitude of dry cycles create cracks and fissures in soil profile [32,33,41,42].
Initially, dense root growth can block soil pores and reduce hydraulic conductivity. Jotisankasa and Sirirattanachat [43] observed that the permeability of soil cultivated with vetiver grass (Chrysopogon zizanioides L.) reached a maximum at rooting densities of 6.5 and 6.0 kg m−3 in a silt soil and clayey sand, respectively; after this, these parameters decreased because roots occupying soil pores prevented water flow. Vergani and Graf [44] noted that Ksat increased initially after planting white alder (Alnus incana L.) but decreased when root volume density surpassed 0.1 cm3 cm−3. However, the maximum effect of root growth on aggregate stability took place at root densities greater than 2 cm3 cm−3. Aggregate stability is conceptually understood to maintain preferential flow paths [45]. However, some studies found an asynchronous relationship between aggregate stability and hydraulic conductivity [46]. Root death and decomposition are necessary for new biopores to emerge and serve as preferential pathways for root growth or water infiltration [33,42,45].
Root system impacts on soil macroporosity have received formidable research attention for their potential to alleviate subsoil compaction [4,12,13,14,18,33,47]. Reintam et al. [48] reported that Cirsium arvense L. reduced penetration resistance on abandoned farmland in Estonia. The root growth of Cirsium arvense demonstrated significant resilience to compaction since it increased root and shoot growth. In general, tap-rooted dicotyledonous species are more successful at penetrating compacted subsoils [37]. Alfalfa’s (Medicago sativa L.) perennial root system has been found to increase soil aggregate stability, hydraulic conductivity, and macroporosity [42,47,49,50,51].
In addition to roots, plant residue and mulches also contribute to changes in soil physical properties through organic matter as the main ingredient for stabilizing soil aggregates [52,53,54,55,56,57]. Abiven et al. [58] reviewed the literature related to aggregate stability changes from different organic inputs. They found a clear separation in both the magnitude of increase in aggregate stability and the time-to-maximum as the organic inputs became more chemically complex. Initial increases in aggregate stability were strongly correlated with evolved CO2, suggesting that microbial activity facilitates soil aggregates’ stabilization. The organic inputs’ chemical properties also stabilize aggregates by increasing inter-aggregate cohesion and hydrophobicity, thus protecting them from water destruction. However, microbial secretions tend to be larger polymers than those secreted by plants and thus are more effective at stabilizing aggregates [59].

2.3. Cover Crops as Physical Modifiers

Cover crops can be employed to provide ecological services that improve soil physical properties and agroecosystems’ broader functioning [4,7]. Using plants to improve the soil environment has been called “bioengineering” [60], and in the context of decompacting the subsoil, “biodrilling” [4]. Yunusa and Newton [7] described this process as “soil priming”, whereby a “primer plant” can benefit the following crop’s yield via modifications to the soil environment. Perenniality, coarse roots, and rapid root turnover rates are characteristics that Yunusa and Newton [7] suggest make for ideal primer plants.
Different species of cover crops can be selected depending on the ecological service to be provided. For example, cover crops with fine root systems increase pore spaces’ heterogeneity in the soil, while coarse root systems predominantly increase soil macroporosity [10]. Nuttall et al. [5] tested the primer-plant hypothesis on several coarse-rooted plants. They found that birds-foot trefoil (Lotus corniculatus L.) and sulla (Hedysarum coronarium L.) increased the following wheat crop’s exploration of the soil and water use efficiency. Similarly, alfalfa increased root growth of wheat in the 0–0.50 m horizons, but this did not result in a significant increase in wheat yield. Perkons et al. [6] compared the root growth of spring wheat and winter barley following two years of either chicory (Cichorium intybus L.) or oat and tall fescue (Avena sativa L.; Festuca arundinacea Schreb.) pre crop. Root growth was significantly greater for both spring wheat and winter barley below 125 cm when following chicory; the effect was more pronounced in winter barley where the roots were longer. Contrary, root exploration in the topsoil was greater following the oat-fescue pre-crop, likely because oat and fescue have concentrated root systems in the upper soil horizons.
Bodner et al. [10] studied the impact of twelve different cover crop species on soil properties using root imaging. A significant positive relationship was found between root volume density (RVD; cm3 cm−3) and field water content (cm3 cm−3) such that at RVDs > 0.004, the water content was maximized at 0.46 cm3 m−3. A mixture of three cover crop species, including cereal rye (Secale cereale L.), crimson clover (Trifolium incarnatum L.), and Hairy Vetch (Vicia villosa L.), along with some brassicas and legumes also maximized these parameters.
Using a sandy loam field soil packed in columns, Burr-Hersey et al. [11] grew commonly used cover crops (tillage radish, common vetch, and black oat) in sandy loam soil to evaluate their potential for biodrilling with X-ray computed tomography. They found that tillage radish and black oats’ roots were able to penetrate a compacted subsoil layer. In contrast, vetch roots were restricted to the uncompacted zone. They concluded that tillage radish and black oats demonstrated root growth behaviors that are likely to reduce subsoil compaction and create biopores. Chen and Weil [13] studied the effect of cover crops on corn root growth under compacted and uncompacted conditions in the subsequent growing season. By counting the number of living roots at soil core break planes, they found that corn when grown after forage radish (Raphanus sativus var. longipinnatus cv. ‘Daikon’) had more roots in the 45 cm soil depth than corn grown after rapeseed (Brassica napus cv. ‘Essex’) or cereal rye. Forage radish has an aggressive taproot that can create subsoil biopores, and corn roots will preferentially colonize these to access subsoil water and nutrients instead of growing through the bulk soil [18]. Williams and Weil [14] monitored colonization of soil biopores by soybean roots using a minirhizotron. They observed that soybean roots colonized biopores left by the previous season’s forage radish cover crop. At one field site where water was limiting, soybean yield was greatest after a canola-cereal rye cover crop where the combined services of biodrilling and reduced evaporative water loss were achieved, thus improving water availability and accessibility of the soybean crop.
Cover crops also improve soil aggregation [46,61,62]. Linsler et al. [63] used aggregate fractionation to determine changes in mean weight diameter for soil aggregates, using 2000, 1000, 250, and 53 µm sieves. They detected a 2-fold increase in large and medium-sized aggregates compared to a fallow treatment after 12 weeks. Steele et al. [46] reported that cover crops positively contribute to organic matter and aggregate stability (AS), and a significant correlation between organic matter and AS had been reported by Blanco-Canqui et al. [64]. Haynes and Beare [65] studied the impact of six cover crops AS, measuring organic matter and microbial biomass. They suggested that AS’s improvements from encouraging microbial activity in the soil produce soil binding agents since AS was more closely related to microbial biomass than root biomass. Drury et al. [66] found improvements in soil physical properties in a no-tillage planting bed when a red clover cover crop was grown. Similarly, Chen et al. [67] found that tap-rooted cover crops in the Brassicaceae family could increase the least limiting water range (LLWR). The LLWR is an important indicator of soil quality and soil physical constraints on crop production. This parameter incorporates crop-limiting values of soil strength, aeration, and water supply to plant roots. Olibone et al. [68] also observed improvements in LLWR with cover crops in no-till, but this did not significantly change soybean yield.

3. Measurement Techniques

With roots being the “hidden half” of plants, they have long been understudied compared to above-ground plant growth and development [69]. To increase our understanding of RS interactions and leverage these principles to improve the management of cover crops and their ecological services, it is worth reviewing a range of RS measurement techniques used in past research. In the first part of this section, measurements that indirectly document RS interactions by measuring changes in roots or soil physical properties will be reviewed; second, those measuring changes in both roots and the soil environment will be reviewed.

3.1. Techniques for Measuring Soil Physical Properties Associated with Roots

Changes in soil physical properties may be measured to infer root growth. For example, infiltrometers and hydraulic conductivity measurements are indirect ways of measuring soil macroporosity changes [70,71,72,73], which can be influenced by root biopores formation [14]. Bulk density, penetration resistance, water retention, and pore-size distribution (PSD) gives further insight into soil particles’ arrangement in the soil profile [13,64,70,74]. Wet aggregate stability is highly correlated with organic matter content but less correlated with infiltration [46,64,75,76]. Studies that have used these techniques to measure changes in soil physical properties from cover crops are listed in Table 1.
A couple of innovative measurement techniques are worth considering for future studies. De Almeida et al. [88] used a large rainfall simulator to find the stable infiltration rate of soils under soybean cultivation, whether conventionally tilled or no-tilled. In this method, infiltration rates can be estimated under field conditions. Surface residue does not need to be removed—unlike some infiltrometers, infiltration is measured according to the difference between incident rainfall and runoff.
Though not receiving much recent attention, changes in root growth across treatments can be obtained indirectly by measuring vertical root-pulling resistance (VRPR) [89]. Beck et al. [90] used VRPR with corn plants to estimate root system mass and root tensile strength more expediently than excavating. A similar technique was previously used in rice (Oryza sativa L.) to measure deep rooting and drought tolerance [91,92,93]. Since VRPR can be used to measure a selection of roots’ morphological attributes, it could be used to measure changes in crop root growth due to changes in soil physical environment influenced by cover crop roots.

3.2. Techniques for Measuring Physical Root–soil Interactions

Root-soil research has predominantly relied upon three approaches to measuring root systems: 1. Excavation of the plant root system, 2. Extracting soil cores or monoliths to be further analyzed in the lab, and 3. Installing in-growth cores to monitor root growth into a given area over time [94,95]. These techniques help to learn about root biomass and length distribution in each area, root morphology, and to a lesser extent, the geometry or architecture of root systems. By taking multiple measurements for a season, one can acquire some level of understanding of root growth or turnover rates over time.
Digging trenches of various depths to expose the soil profile allows for several possible measurements to be taken. Perkons et al. [6] excavated a 2 m deep trench to measure rooting depth and spring wheat density following different pre-crops. To measure the presence of biopores, the field can be excavated to a given depth (ex. 45 cm) to clear a horizontal area where pores of a given size class are counted [6,47]. The horizontal area is often cleaned with a vacuum cleaner to leave a clear surface. The extraction of soil monoliths for analysis in the lab affords opportunities for both old and new measurement techniques to be used. Root length density and other physical parameters can be gleaned from washing the roots from soil cores and using image scanning technologies such as WinRhizo [10]. A pinboard can be inserted into a monolith and used to maintain the roots’ spatial distribution as they are washed; this can indicate the root system architecture without spending much time in the field [96]. By horizontally breaking soil cores taken at a given depth, one can count the abundance of living roots protruding from the break’s surfaces and infer root growth at different depths [13,97]. By vertically breaking soil monoliths, root growth inside biopores can be visually assessed, and high-resolution image analysis techniques can be incorporated to analyze 2D root growth [98]. Soil cores can also be impregnated with fluorescent resins for further scanning to study pore size, shape, and distribution [84].
Rhizotrons have been frequently used to study RS interactions because field rhizotrons and observation windows, like in-growth cores, allow for repeated study of root growth over time [95,96]. Minirhizotrons have been used to study root growth and root colonization of soil macropores and biopores in the field [14,42]. However, there are significant challenges associated with in situ rhizotrons. First, to get a representative understanding of a plant’s root system, many observation ports—whether tubes or windows—need to be installed. This installation process causes significant disruption of the soil profile. A recommended period of 1–2 years ought to pass to re-establish a natural soil structure before measurements are taken [96]. Perhaps the greatest challenge is to ensure that roots or biopores will emerge near the observation port to capture root growth [99]. Though, this may be partially overcome by hand-planting directly over the installed rhizotron [14].
The setbacks of rhizotrons can also be overcome by using in situ endoscopy [99]. Athmann et al. [100] excavated a trench, dug horizontally into the soil profile at a depth of 105 cm, and used a mini videoscope to probe the insides of macropores >5 mm. below the free-hanging horizontal surface avoiding inside disruption of the biopores compared to inserting the videoscope from above. The videoscope could be inserted up to 10 cm into the biopore, and then the 10 cm section was cleared away for the videoscope to probe the next section above. Cover crop studies that used these techniques are listed in Table 2.
Tomography: All the techniques mentioned above require significant amounts of time spent either in the field or in the lab destructively hand-processing soil monoliths. With 3-dimensional tomographic technology, time may be redirected to digital image processing, and non-destructive sampling is possible with pot-based experiments. X-ray computed tomography (XCT) has received the most research attention [101], though neutron computed tomography (NCT), magnetic resonance imaging (MRI), and ground-penetrating radar (GPR) are being explored to counteract the challenges associated with XCT. XCT has been used most extensively to study pore networks in soil, specifically from earthworm activity [73,102]. Still, others have used it as a non-destructive method to study soil-structure [103], aggregation [104], and RS interactions [103,105,106]. All tomographic technologies require a sufficient level of contrast between the phase elements (e.g., soil, water, plant roots) to be distinguished. In XCT, X-ray beams are attenuated by materials of differing density and atomic number [107]. Thus, materials with similar physicochemical properties will have similar X-ray absorption and be more difficult to distinguish.
Sufficient contrast can be achieved when comparing air-filled pore space with the surrounding soil matrix; to this end, Munkholm et al. [108] successfully characterized soil pore space across different crop rotations and tillage management. On the other hand, sufficient contrast is not easily achieved when comparing plant roots and water-filled pores. In a pot-based experiment, Metzner et al. [109] found that increasing the pot size from 3.4 × 20 cm (“diameter” × “height”) to 8.1 × 30 cm increased the amount of water pooling and decreased the effectiveness of XCT at capturing the root system. Contrast can be improved by using higher-resolution synchrotron microtomography, which, because of its higher power, increases monochromatization of the X-ray beams [110,111]. Some researchers have used contrast enhancement filters on open-source image analysis software like ImageJ to improve image quality [103]. However, this is unlikely to solve the challenges of larger pot sizes.
NCT differs from XCT as it measures the attenuation of charged neutrons instead of protons by various materials [112]. As NCT is highly sensitive to low molecular weight compounds, normal water results in a lot of beam hardening and scattering—two alternate fates of the imaging beams other than attenuation. Tumlinson et al. [112] found that sample sizes cannot exceed 2.5 cm in diameter if there is any water present. Meyer et al. [113] found that NCT accurately estimated soil pore size distribution except in soils with high clay or organic matter contents due to high shrinking/swelling potential. An additional challenge is the requirement of a radiation use permit at facilities carrying out this type of analysis. Another alternative is the MRI technology that measures radio waves’ attenuation instead of photons or neutrons and provides high contrast between water and roots. Metzner et al. [109] found that MRI could detect roots in saturated conditions where XCT was not, but it did not have as high resolution and so at times overestimated root length. Additionally, the MRI is restricted to soils with low iron levels [114].
GPR uses electromagnetic pulses sent from an antenna moved over the soil surface to detect the magnitude of reflected signals based on the nature of material below the soil surface. Processing the original radargrams requires five steps: radargram standardization, noise reduction, signal amplification, migration (Kirchoff), and Hilbert transformation [115]. Standardization of GPR indexes for estimating coarse root size and root biomass is still in its infancy. However, several studies have accurately estimated these parameters in the field [116,117,118]. It is limited based on the depth of analysis since reflected radio signals will grow weaker as depth increases. Some studies have also used GPR to measure soil water concentrations [119,120].
Alternative Methods: A couple of technologies offer unique insights into high-contrast imaging of root architectures, though not offering direct insight into cover crop RS interactions. Piñeros et al. [121] grew plants hydroponically on a vertical structure of mesh discs; the discs encouraged a more soil-like growth habit and helped retain the root system’s shape for imaging. Additionally, Downie et al. [106] tested the substrate Nafion for use as a transparent soil and found it behaved similarly to vermiculite. Due to both Nafion and water’s similar refractive indices, plants can be grown in a soil-like medium, and root architectures and morphology can be easily imaged. In both technologies, applications for RS interactions with cover crops are limited because they do not behave like soil. Nevertheless, they may differentiate root morphologies between cover crops and efficiently isolate species and cultivars to be further studied under field conditions.
Recently, a procedure for visual estimation of soil structure (VESS) has been standardized by Ball et al. [122]. Considering parameters such as soil strength, structure, porosity, and biological activity, Munkholm et al. [108] found that VESS values were highly correlated with corn yield at a long-term rotation-tillage trial. These sorts of “quick and dirty” methods have been used to successfully identify differences in soil quality due to tillage and cropping history [122]. They may be applied to cover crop systems to identify broad differences in treatments with a range of RS interactions.

4. Improving Future Root-Soil Research with New Technologies

After reviewing the studies on cover crop root–soil interactions (Table 1), what becomes clear is that observed changes in some soil physical properties are less consistent than others. That cover crops positively affect aggregate stability is well-established; that cover crops modify the soil’s bulk density, strength, and water-holding capacity less clearly. Even studies that measured these parameters over several years of cover crop use did not detect significant changes [70]. Then, again, the likelihood of detecting changes in soil physical properties is highly dependent on the cover crop being studied. Unlike tap-rooted cover crops cereal rye is not likely to produce changes in field water content [10] or soil macroporosity [12] but may increase the PSD [32,41].
For this reason, procedures that first validate in a controlled environment the ecological service in question across a range of plant species may increase the efficiency of cover crop research effort. Transparent growth media [106,121] and the use of tomographic technologies to sequentially image root system growth and development [11] can be used to characterize cover crop root system architecture and response to different soil physical properties. The study by Burr-Hersey et al. [11] is particularly insightful in that Vetch (Vicia sativa) root growth did not respond favorably to soil compaction; black oats, on the contrary, demonstrated substantial resilience to compaction but has yet to be considered in the field studies on cover crop RS interactions. According to several field studies, Vetch produced some or no change in soil strength and compaction [64,76,78,79,81]. Still, the ecological service being studied (i.e., reduced soil compaction) should not be expected to change greatly since Vetch is not a fitting species to maximize this service provision. Thus, a better understanding of the RS interactions of a range of species used as cover crops is needed to design cover crops for better experiments.
Tomographic imaging allows studying root system growth under field conditions. Colombi et al. [18] sampled soil from wheat, corn, and soybean plots and successfully used XCT imaging to measure artificial macropores’ exploitation rate by plant roots. This cannot perfectly substitute for the detail and insight gained through macropore endoscopy [100]—nor vice versa—but there are some obvious advantages. First, while endoscopy identifies macropore colonization, it does not additionally provide a map of the root system network; XCT can provide both, albeit on a smaller scale given size restrictions on the cores to be scanned. Second, endoscopy remains a highly destructive sampling method requiring full excavation of a soil trench and soil layers permeated by macropore channels. Reduced time spent in the field may be the largest barrier to expanded use of endoscopy. Nevertheless, endoscopy is an innovative measurement technique that cannot presently be replaced by tomography.
Tomographic imaging also provides an opportunity for high-resolution, non-destructive measurement of soil particle arrangement and pore space, which may clarify the variability or lack of significant differences found when measuring various soil physical properties. Demonstrating this, Cercioglu et al. [101] sampled soil from cover crop and non-cover crop treatments for XCT scanning in addition to constant head Ksat measurements. While they found a significantly greater number of XCT-measured macropores under the cover crop treatment, and while the number of macropores was positively correlated with Ksat, they could not detect a significant difference Ksat between the treatments. This ought to caution conclusions based on Ksat or other infiltration estimates of macroporosity since differences may be immediately absent from Ksat values but exposed through a more detailed analysis.
Finally, studies that integrate several parameters may better detect changes in soil physical properties from cover crop RS interactions. To this end, the work of Chen et al. [67] and Olibone et al. [68] in measuring changes to the LLWR from cover crops stands out. In contrast, many other studies measure the same parameters and considered alone, they do not provide as holistic an explanation of changes to soil physical properties produced by cover crops and how this may affect subsequent crop production. Like the LLWR, VESS scores integrate several parameters and may be suitable for cover crop studies. Though not entirely a cover crop system, the study by Liesch et al. [123] provides some indication that changes in VESS scores can be observed after several months of fall cereal rye growth. With their study, significant VESS changes were observed even when there were no significant differences in infiltration rates across treatments. Since infiltration rate is influenced by several factors [123] and depends on the time of year when sampling takes place [32,41,46,84]. Larger infiltration systems de Almeida et al. [88] that simulate field conditions may be better suitable to account for natural variability than small soil cores or infiltrometers. This is yet to be determined.

5. Conclusions

Cover crop RS interactions have been studied indirectly by measuring changes in soil physical properties, which have yielded varying results. The LLWR and VESS should be applied in future studies since they integrate several parameters into one metric. They should provide a more holistic explanation of soil physical properties’ changes and suitability for crop production induced by cover crops. A few studies have labored to study cover crop and production crop roots in concert with changes in soil physical properties, and these have been highly insightful. Moving ahead, tomographic imaging technologies can be used to improve research quality through both these avenues of inquiry: when indirectly studying RS interaction through changes in soil physical properties, tomographic imaging increases the detail of analysis and can reveal treatment differences that were unobservable by other means; and when studying the direct interaction of roots and soil in cover crop systems, tomography allows for non-destructive, repeated imaging of root system growth and development in controlled environments, while also being employable to explore root networks under field conditions. Controlled environment studies should not be neglected. Cover crops may be more efficiently studied in controlled environments to substantiate which species most likely maximize the desired service provision. More comparative studies like those of Lal et al. [85], Bodner et al. [10], and Burr-Hersey et al. [11] that group plant species based on service provisions are needed.

Author Contributions

A.B. conceptualized the framework and supervised the overall project. C.M.O. prepared the first draft. W.A. and H.B.V. reviewed and edited the manuscript for submission. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study did not involve human or animals.

Data Availability Statement

Data will be made available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kenrick, P.; Strullu-Derrien, C. The Origin and Early Evolution of Roots. Plant Physiol. 2014, 166, 570–580. [Google Scholar] [CrossRef] [Green Version]
  2. Gregory, P.J. Plants, Roots and the Soil. In Plant Roots; Gregory, P.J., Ed.; Blackwell Publishing Ltd.: Hoboken, NJ, USA, 2007; pp. 1–17. ISBN 9780470995563. [Google Scholar]
  3. Lefroy, E.C.; Hobbs, R.J.; O’Connor, M.H.; Pate, J.S. Agriculture as a Mimic of Natural Ecosystems; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1999. [Google Scholar]
  4. Cresswell, H.P.; Kirkegaard, J.A. Subsoil amelioration by plant roots—The process and the evidence. Aust. J. Soil Res. 1995, 33, 221–239. [Google Scholar] [CrossRef]
  5. Nuttall, J.G.; Davies, S.L.; Armstrong, R.A.; Peoples, M.B. Testing the primer-plant concept: Wheat yields can be increased on alkaline sodic soils when an effective primer phase is used. Aust. J. Agric. Res. 2008, 59, 331–338. [Google Scholar] [CrossRef]
  6. Perkons, U.; Kautz, T.; Uteau, D.; Peth, S.; Geier, V.; Thomas, K.; Lütke Holz, K.; Athmann, M.; Pude, R.; Köpke, U. Root-length densities of various annual crops following crops with contrasting root systems. Soil Tillage Res. 2014, 137, 50–57. [Google Scholar] [CrossRef]
  7. Yunusa, I.A.M.; Newton, P.J. Plants for amelioration of subsoil constraints and hydrological control: The primer-plant concept. Plant Soil 2003, 257, 261–281. [Google Scholar] [CrossRef]
  8. Abdalla, M.; Hastings, A.; Cheng, K.; Yue, Q.; Chadwick, D.; Espenberg, M.; Truu, J.; Rees, R.M.; Smith, P. A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity. Glob. Chang. Biol. 2019, 25, 2530–2543. [Google Scholar] [CrossRef] [Green Version]
  9. Garcia-Franco, N.; Hobley, E.; Hübner, R.; Wiesmeier, M. Climate-Smart Soil Management in Semiarid Regions. In Soil Management and Climate Change; Academic Press: Cambridge, MA, USA, 2018; ISBN 9780128121290. [Google Scholar]
  10. Bodner, G.; Leitner, D.; Kaul, H.-P. Coarse and fine root plants affect pore size distributions differently. Plant Soil 2014, 380, 133–151. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Burr-Hersey, J.E.; Mooney, S.J.; Bengough, A.G.; Mairhofer, S.; Ritz, K. Developmental morphology of cover crop species exhibit contrasting behaviour to changes in soil bulk density, revealed by X-ray computed tomography. PLoS ONE 2017, 12, e0181872. [Google Scholar]
  12. Chen, G.; Weil, R.R. Penetration of cover crop roots through compacted soils. Plant Soil 2010, 331, 31–43. [Google Scholar] [CrossRef]
  13. Chen, G.; Weil, R.R. Root growth and yield of maize as affected by soil compaction and cover crops. Soil Tillage Res. 2011, 117, 17–27. [Google Scholar] [CrossRef]
  14. Williams, S.M.; Weil, R.R. Crop cover root channels may alleviate soil compaction effects on soybean crop. Soil Sci. Soc. Am. J. 2004, 68, 1403–1409. [Google Scholar] [CrossRef]
  15. Harber, A.; Rogers, G.; Tan, D.K.Y. The effect of cover crops on physical, chemical and microbial properties of a sandy loam soil and baby leaf spinach yield. In Proceedings of the 18th Australian Agronomy Conference 2017, Ballarat, Australia, 24–28 September 2017. [Google Scholar]
  16. Irmak, S.; Sharma, V.; Mohammed, A.T.; Djaman, K. Impacts of cover crops on soil physical properties: Field capacity, permanent wilting point, soil-water holding capacity, bulk density, hydraulic conductivity, and inflitration. Trans. ASABE 2018, 61, 1307–1321. [Google Scholar] [CrossRef]
  17. Alvarez, R.; Steinbach, H.S.; De Paepe, J.L. Cover crop effects on soils and subsequent crops in the pampas: A meta-analysis. Soil Tillage Res. 2017, 170, 53–65. [Google Scholar] [CrossRef]
  18. Colombi, T.; Braun, S.; Keller, T.; Walter, A. Artificial macropores attract crop roots and enhance plant productivity on compacted soils. Sci. Total Environ. 2017, 574, 1283–1293. [Google Scholar] [CrossRef] [Green Version]
  19. Brouwer, R. Functional equilibrium: Sense or nonsense? Netherlands J. Agric. Sci. 1983, 31, 335–348. [Google Scholar] [CrossRef]
  20. Wolters, H.; Jürgens, G. Survival of the flexible: Hormonal growth control and adaptation in plant development. Nat. Rev. Genet. 2009, 10, 305–317. [Google Scholar] [CrossRef]
  21. Hodge, A.; Berta, G.; Doussan, C.; Merchan, F.; Crespi, M. Plant root growth, architecture and function. Plant Soil 2009, 321, 153–187. [Google Scholar] [CrossRef]
  22. Giehl, R.F.H.; von Wirén, N. Root Nutrient Foraging. Plant Physiol. 2014, 166, 509–517. [Google Scholar] [CrossRef] [Green Version]
  23. Pfeifer, J.; Kirchgessner, N.; Walter, A. Artificial pores attract barley roots and can reduce artifacts of pot experiments. J. Plant Nutr. Soil Sci. 2014, 177, 903–913. [Google Scholar] [CrossRef]
  24. Bao, Y.; Aggarwal, P.; Robbins, N.E.; Sturrock, C.J.; Thompson, M.C.; Tan, H.Q.; Tham, C.; Duan, L.; Rodriguez, P.L.; Vernoux, T.; et al. Plant roots use a patterning mechanism to position lateral root branches toward available water. Proc. Natl. Acad. Sci. USA 2014, 111, 9319–9324. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Pierret, A.; Doussan, C.; Capowiez, Y.; Bastardie, F.; Pagès, L. Root Functional Architecture: A Framework for Modeling the Interplay between Roots and Soil. Vadose Zone J. 2007, 6, 269–281. [Google Scholar] [CrossRef]
  26. McMichael, B.L.; Quisenberry, J.E. The impact of the soil environment on the growth of root systems. Environ. Exp. Bot. 1993, 33, 53–61. [Google Scholar] [CrossRef]
  27. Benjamin, J.G.; Nielsen, D.C. Water deficit effects on root distribution of soybean, field pea and chickpea. Field Crop. Res. 2006, 97, 248–253. [Google Scholar] [CrossRef]
  28. Benjamin, J. Effect of Soil Attributes on Root Growth and Distribution in Some Common Crops: A Synthesis of Knowledge and Future Needs. In Enhancing Understanding and Quantification of Soil–Root Growth Interactions; Timlin, D., Ahuja, L.R., Eds.; American Society of Agronomy, Crop Science Society of America, Soil Science Society of America: Madison, WI, USA, 2013; pp. 31–43. ISBN 978-0-89118-339-6. [Google Scholar]
  29. Kolb, E.; Valérie, L.; Marie-Béatrice, B.-T. Physical root–soil interactions. Phys. Biol. 2017, 14, 65004. [Google Scholar] [CrossRef]
  30. Logsdon, S.D.; Parker, J.C.; Reneau, R.B. Root growth as influenced by aggregate size. Plant Soil 1987, 99, 267–275. [Google Scholar] [CrossRef]
  31. Stirzaker, R.J.; Passioura, J.B.; Wilms, Y. Soil structure and plant growth: Impact of bulk density and biopores. Plant Soil 1996, 185, 151–162. [Google Scholar] [CrossRef]
  32. Bodner, G.; Scholl, P.; Kaul, H.P. Field quantification of wetting–drying cycles to predict temporal changes of soil pore size distribution. Soil Tillage Res. 2013, 133, 1–9. [Google Scholar] [CrossRef] [Green Version]
  33. Dexter, A.R. Amelioration of soil by natural processes. Soil Tillage Res. 1991, 20, 87–100. [Google Scholar] [CrossRef]
  34. Stagnari, F.; Maggio, A.; Galieni, A.; Pisante, M. Multiple benefits of legumes for agriculture sustainability: An overview. Chem. Biol. Technol. Agric. 2017, 4, 1–13. [Google Scholar] [CrossRef] [Green Version]
  35. Celette, F.; Gaudin, R.; Gary, C. Spatial and temporal changes to the water regime of a Mediterranean vineyard due to the adoption of cover cropping. Eur. J. Agron. 2008, 29, 153–162. [Google Scholar] [CrossRef]
  36. Bodner, G.; Nakhforoosh, A.; Kaul, H.P. Management of crop water under drought: A review. Agron. Sustain. Dev. 2015, 35, 401–442. [Google Scholar] [CrossRef]
  37. Kautz, T. Research on subsoil biopores and their functions in organically managed soils: A review. Renew. Agric. Food Syst. 2014, 30, 318–327. [Google Scholar] [CrossRef]
  38. Logsdon, S.D. Root Effects on Soil Properties and Processes: Synthesis and Future Research Needs. In Enhancing Understanding and Quantification of Soil–Root Growth Interactions; Timlin, D., Ahuja, L.R., Eds.; American Society of Agronomy, Crop Science Society of America, Soil Science Society of America: Madison, WI, USA, 2013; pp. 173–196. ISBN 978-0-89118-339-6. [Google Scholar]
  39. Ontl, T.A.; Hofmockel, K.S.; Cambardella, C.A.; Schulte, L.A.; Kolka, R.K. Topographic and soil influences on root productivity of three bioenergy cropping systems. New Phytol. 2013, 199, 727–737. [Google Scholar] [CrossRef] [PubMed]
  40. Dexter, A.R. Compression of soil around roots. Plant Soil 1987, 97, 401–406. [Google Scholar] [CrossRef]
  41. Bodner, G.; Scholl, P.; Loiskandl, W.; Kaul, H.P. Environmental and management influences on temporal variability of near saturated soil hydraulic properties. Geoderma 2013, 204–205, 120–129. [Google Scholar] [CrossRef]
  42. Rasse, D.P.; Smucker, A.J.M.; Santos, D. Alfalfa Root and Shoot Mulching Effects on Soil Hydraulic Properties and Aggregation. Soil Sci. Soc. Am. J. 2000, 64, 725–731. [Google Scholar] [CrossRef]
  43. Jotisankasa, A.; Sirirattanachat, T. Effects of grass roots on soil-water retention curve and permeability function. Can. Geotech. J. 2017, 54, 1612–1622. [Google Scholar] [CrossRef] [Green Version]
  44. Vergani, C.; Graf, F. Soil permeability, aggregate stability and root growth: A pot experiment from a soil bioengineering perspective. Ecohydrology 2016, 9, 830–842. [Google Scholar] [CrossRef]
  45. Ghestem, M.; Sidle, R.C.; Stokes, A. The Influence of Plant Root Systems on Subsurface Flow: Implications for Slope Stability. Bioscience 2011, 61, 869–879. [Google Scholar] [CrossRef]
  46. Steele, M.K.; Coale, F.J.; Hill, R.L. Winter annual cover crop impacts on no-till soil physical properties and organic matter. Soil Sci. Soc. Am. J. 2012, 76, 2164–2173. [Google Scholar] [CrossRef]
  47. Gaiser, T.; Perkons, U.; Küpper, P.M.; Puschmann, D.U.; Peth, S.; Kautz, T.; Pfeifer, J.; Ewert, F.; Horn, R.; Köpke, U. Evidence of improved water uptake from subsoil by spring wheat following lucerne in a temperate humid climate. Field Crop. Res. 2012, 126, 56–62. [Google Scholar] [CrossRef]
  48. Reintam, E.; Trükmann, K.; Kuht, J. Effect of Cirsium arvense L. on soil physical properties and crop growth. Agric. Food Sci. 2008, 17, 153–164. [Google Scholar] [CrossRef] [Green Version]
  49. Huang, Z.; Sun, L.; Liu, Y.; Liu, Y.F.; López-Vicente, M.; Wei, X.H.; Wu, G.L. Alfalfa planting significantly improved alpine soil water infiltrability in the Qinghai-Tibetan Plateau. Agric. Ecosyst. Environ. 2019, 285, 106606. [Google Scholar] [CrossRef]
  50. Guo, L.; Liu, Y.; Wu, G.L.; Huang, Z.; Cui, Z.; Cheng, Z.; Zhang, R.Q.; Tian, F.P.; He, H. Preferential water flow: Influence of alfalfa (Medicago sativa L.) decayed root channels on soil water infiltration. J. Hydrol. 2019, 578, 1–8. [Google Scholar] [CrossRef]
  51. Meek, B.D.; Rechel, E.R.; Carter, L.M.; DeTar, W.R.; Urie, A.L. Infiltration Rate of a Sandy Loam Soil: Effects of Traffic, Tillage, and Plant Roots. Soil Sci. Soc. Am. J. 1992, 56, 908–913. [Google Scholar] [CrossRef]
  52. Si, P.; Liu, E.; He, W.; Sun, Z.; Dong, W.; Yan, C.; Zhang, Y. Effect of no-tillage with straw mulch and conventional tillage on soil organic carbon pools in Northern China. Arch. Agron. Soil Sci. 2018, 64, 398–408. [Google Scholar] [CrossRef]
  53. Jordán, A.; Zavala, L.M.; Gil, J. Effects of mulching on soil physical properties and runoff under semi-arid conditions in southern Spain. Catena 2010, 81, 77–85. [Google Scholar] [CrossRef]
  54. Mupangwa, W.; Twomlow, S.; Hove, L.; Walker, S.; Africa, S. Effect of mulching and minimum tillage on maize (Zea mays L.) yield and water content of clayey and sandy soils. Phys. Chem. Earth 2007, 32, 1127–1134. [Google Scholar] [CrossRef]
  55. Larney, F.J.F.J.; Angers, D.A.D.A. The role of organic amendments in soil reclamation: A review. Can. J. Soil Sci. 2012, 92, 19–38. [Google Scholar] [CrossRef]
  56. Mulumba, L.N.; Lal, R. Mulching effects on selected soil physical properties. Soil Tillage Res. 2008, 98, 106–111. [Google Scholar] [CrossRef]
  57. Tisdall, J.M.; Oades, J.M. Organic matter and water-stable aggregates in soils. J. Soil Sci. 1982, 33, 141–163. [Google Scholar] [CrossRef]
  58. Abiven, S.; Menasseri, S.; Chenu, C. The effects of organic inputs over time on soil aggregate stability—A literature analysis. Soil Biol. Biochem. 2009, 41, 1–12. [Google Scholar] [CrossRef]
  59. Goss, M.J.; Kay, B.D. Soil Aggregation. In Roots and Soil Management: Interactions between Roots and the Soil; Zobel, R.W., Wright, S.F., Eds.; American Society of Agronomy, Crop Science Society of America, Soil Science Society of America: Madison, WI, USA, 2005; pp. 163–180. ISBN 978-0-89118-267-2. [Google Scholar]
  60. Verboom, W.H.; Pate, J.S. Bioengineering of soil profiles in semiarid ecosystems: The ‘phytotarium’ concept. A review. Plant Soil 2006, 289, 71–102. [Google Scholar] [CrossRef]
  61. Nouri, A.; Lee, J.; Yin, X.; Tyler, D.D.; Saxton, A.M. Thirty-four years of no-tillage and cover crops improve soil quality and increase cotton yield in Alfisols, Southeastern USA. Geoderma 2019, 337, 998–1008. [Google Scholar] [CrossRef]
  62. Abdollahi, L.; Munkholm, L.J. Tillage system and cover crop effects on soil quality: I. chemical, mechanical, and biological properties. Soil Sci. Soc. Am. J. 2014, 78, 262–270. [Google Scholar] [CrossRef]
  63. Linsler, D.; Kaiser, M.; Andruschkewitsch, R.; Piegholdt, C.; Ludwig, B. Effects of cover crop growth and decomposition on the distribution of aggregate size fractions and soil microbial carbon dynamics. Soil Use Manag. 2016, 32, 192–199. [Google Scholar] [CrossRef]
  64. Blanco-Canqui, H.; Mikha, M.M.; Presley, D.R.; Claassen, M.M. Addition of cover crops enhances no-till potential for improving soil physical properties. Soil Sci. Soc. Am. J. 2011, 75, 1471–1482. [Google Scholar] [CrossRef]
  65. Haynes, R.J.; Beare, M.H. Influence of six crop species on aggregate stability and some labile organic matter fractions. Soil Biol. Biochem. 1997, 29, 1647–1653. [Google Scholar] [CrossRef]
  66. Drury, C.F.; Tan, C.-S.; Welacky, T.W.; Oloya, T.O.; Hamill, A.S.; Weaver, S.E. Red Clover and Tillage Influence on Soil Temperature, Water Content, and Corn Emergence. Agron. J. 1999, 91, 101–108. [Google Scholar] [CrossRef]
  67. Chen, C.; Whalen, J.K.; Guo, X. Earthworms reduce soil nitrous oxide emissions during drying and rewetting cycles. Soil Biol. Biochem. 2014, 68, 117–124. [Google Scholar] [CrossRef]
  68. Olibone, D.; Encide-Olibone, A.P.; Rosolem, C.A. Least limiting water range and crop yields as affected by crop rotations and tillage. Soil Use Manag. 2010, 26, 485–493. [Google Scholar] [CrossRef]
  69. Lux, A.; Rost, T.L. Plant root research: The past, the present and the future. Ann. Bot. 2012, 110, 201–204. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  70. Rorick, J.D.; Kladivko, E.J. Cereal rye cover crop effects on soil carbon and physical properties in southeastern Indiana. J. Soil Water Conserv. 2017, 72, 260–265. [Google Scholar] [CrossRef] [Green Version]
  71. Daryanto, S.; Fu, B.; Wang, L.; Jacinthe, P.A.; Zhao, W. Quantitative synthesis on the ecosystem services of cover crops. Earth-Sci. Rev. 2018, 185, 357–373. [Google Scholar] [CrossRef]
  72. Munkholm, L.J.; Heck, R.J.; Deen, B.; Zidar, T. Relationship between soil aggregate strength, shape and porosity for soils under different long-term management. Geoderma 2016, 268, 52–59. [Google Scholar] [CrossRef]
  73. Warner, G.S.; Nieber, J.L.; Moore, I.D.; Geise, R.A. Characterizing Macropores in Soil by Computed Tomography. Soil Sci. Soc. Am. J. 1989, 53, 653–660. [Google Scholar] [CrossRef]
  74. Haruna, S.I.; Anderson, S.H.; Nkongolo, N.V.; Zaibon, S. Soil hydraulic properties: Influence of tillage and cover crops. Pedosphere 2018, 28, 430–442. [Google Scholar] [CrossRef]
  75. Sainju, U.M.; Singh, B.P.; Whitehead, W.F. Cover crops and nitrogen fertilization effects on soil carbon and nitrogen and tomato yield. Can. J. Soil Sci. 2000, 80, 523–532. [Google Scholar] [CrossRef]
  76. Villamil, M.B.; Bollero, G.A.; Darmody, R.G.; Simmons, F.W.; Bullock, D.G. No-Till Corn/Soybean Systems Including Winter Cover Crops. Soil Sci. Soc. Am. J. 2006, 70, 1936–1944. [Google Scholar] [CrossRef]
  77. Chen, G.; Weil, R.R.; Hill, R.L. Effects of compaction and cover crops on soil least limiting water range and air permeability. Soil Tillage Res. 2014, 136, 61–69. [Google Scholar] [CrossRef]
  78. Patrick, W.H.; Haddon, C.B.; Hendrix, J.A.; Patrick, W.H., Jr.; Haddon, C.B.; Hendrix, J.A.; Patrick, W.H.; Haddon, C.B.; Hendrix, J.A. The Effect of Longtime Use of Winter Cover Crops on Certain Physical Properties of Commerce Loam1. Soil Sci. Soc. Am. J. 1957, 21, 366–368. [Google Scholar] [CrossRef]
  79. Wagger, M.G.; Denton, H.P. Influence of cover crop and wheel traffic on soil physical properties in continuous no-till corn. Soil Sci. Soc. Am. J. 1989, 53, 1206–1210. [Google Scholar] [CrossRef]
  80. Marshall, M.W.; Williams, P.; Nafchi, A.M.; Maja, J.M.; Payero, J.; Mueller, J.; Khalilian, A. Influence of tillage and deep rooted cool season cover crops on soil properties, pests, and yield responses in cotton. Open J. Soil Sci. 2016, 6, 149–158. [Google Scholar] [CrossRef] [Green Version]
  81. Folorunso, O.A.; Rolston, D.E.; Prichard, T.; Loui, D.T. Soil surface strength and infiltration rate as affected by winter cover crops. Soil Technol. 1992, 5, 189–197. [Google Scholar] [CrossRef]
  82. Liu, A.; Ma, B.L.; Bomke, A.A. Effects of cover crops on soil aggregate stability, total organic carbon, and polysaccharides. Soil Sci. Soc. Am. J. 2005, 69, 2041–2048. [Google Scholar] [CrossRef]
  83. McVay, K.A.; Radcliffe, D.E.; Hargrove, W.L. Winter legume effects on soil properties and nitrogen fertilizer requirements. Soil Sci. Soc. Am. J. 1989, 53, 1856–1862. [Google Scholar] [CrossRef]
  84. Carof, M.; De Tourdonnet, S.; Coquet, Y.; Hallaire, V.; Roger-Estrade, J. Hydraulic conductivity and porosity under conventional and no-tillage and the effect of three species of cover crop in northern France. Soil Use Manag. 2007, 23, 230–237. [Google Scholar] [CrossRef]
  85. Lal, R.; Wilson, G.F.; Okigbo, B.N. Changes in properties of an Alfisol produced by various crop covers. Soil Sci. 1979, 127, 377–382. [Google Scholar] [CrossRef]
  86. Kahimba, F.C.; Ranjan, R.S.; Froese, J.; Entz, M.; Nason, R. Cover crop effects on infiltration, soil temperature, and soil moisture distribution in the Canadian Prairies. Appl. Eng. Agric. 2008, 24, 321–333. [Google Scholar] [CrossRef]
  87. Kaspar, T.C.; Radke, J.K.; Laflen, J.M. Small grain cover crops and wheel traffic effects on infiltration, runoff, and erosion. J. Soil Water Conserv. 2001, 56, 160–164. [Google Scholar]
  88. De Almeida, W.S.; Panachuki, E.; de Oliveira, P.T.S.; da Silva Menezes, R.; Sobrinho, T.A.; de Carvalho, D.F. Effect of soil tillage and vegetal cover on soil water infiltration. Soil Tillage Res. 2018, 175, 130–138. [Google Scholar] [CrossRef]
  89. Beck, D.L.; Darrah, L.L.; Zuber, M.S. An improved technique for measuring resistance to root pulling in maize. Crop Sci. 1987, 27, 356–358. [Google Scholar] [CrossRef]
  90. Beck, D.L.; Darrah, L.L.; Zuber, M.S. Relationship of Root Tensile Strength to Vertical Root Pulling Resistance in Maize. Crop Sci. 1988, 28, 571–573. [Google Scholar] [CrossRef]
  91. O’Toole, J.C. Soemartono Evaluation of a simple technique for characterizing rice root systems in relation to drought resistance. Euphytica 1981, 30, 283–290. [Google Scholar] [CrossRef]
  92. Ekanayake, I.J.; Garrity, D.P.; O’Toole, J.C. Influence of Deep Root Density on Root Pulling Resistance in Rice. Crop Sci. 1986, 26, 1181–1186. [Google Scholar] [CrossRef]
  93. Ekanayake, I.J.; Garrity, D.P.; Masajo, T.M.; O’Toole, J.C. Root pulling resistance in rice: Inheritance and association with drought tolerance. Euphytica 1985, 34, 905–913. [Google Scholar] [CrossRef]
  94. Luster, J.; Finlay, R. Handbook of Methods Used in Rhizosphere Research; Swiss Federal Research Institute: WSL, Birmensdorf, Switzerland, 2006. [Google Scholar]
  95. Neumann, G.; George, T.S.; Plassard, C. Strategies and methods for studying the rhizosphere—the plant science toolbox. Plant Soil 2009, 321, 431–456. [Google Scholar] [CrossRef]
  96. Judd, L.A.; Jackson, B.E.; Fonteno, W.C. Advancements in Root Growth Measurement Technologies and Observation Capabilities for Container-Grown Plants. Plants 2015, 4, 369–392. [Google Scholar] [CrossRef] [Green Version]
  97. Kirkegaard, J.A.; Lilley, J.M. Root penetration rate a benchmark to identify soil and plant limitations to rooting depth in wheat. Aust. J. Exp. Agric. 2007, 47, 590–602. [Google Scholar] [CrossRef]
  98. White, R.G.; Kirkegaard, J.A. The distribution and abundance of wheat roots in a dense, structured subsoil—Implications for water uptake. Plant. Cell Environ. 2010, 33, 133–148. [Google Scholar] [CrossRef] [PubMed]
  99. Kautz, T.; Köpke, U. In situ endoscopy: New insights to root growth in biopores. Plant Biosyst. Int. J. Deal. Asp. Plant Biol. 2010, 144, 440–442. [Google Scholar] [CrossRef]
  100. Athmann, M.; Kautz, T.; Pude, R.; Köpke, U. Root growth in biopores—Evaluation with in situ endoscopy. Plant Soil 2013, 371, 179–190. [Google Scholar] [CrossRef]
  101. Cercioglu, M.; Anderson, S.H.; Udawatta, R.P.; Haruna, S.I. Effects of cover crop and biofuel crop management on computed tomography-measured pore parameters. Geoderma 2018, 319, 80–88. [Google Scholar] [CrossRef]
  102. Taina, I.A.; Heck, R.J.; Elliot, T.R. Application of X-ray computed tomography to soil science: A literature review. Can. J. Soil Sci. 2008, 88, 1–19. [Google Scholar] [CrossRef]
  103. Tracy, S.R.; Black, C.R.; Roberts, J.A.; Sturrock, C.; Mairhofer, S.; Craigon, J.; Mooney, S.J. Quantifying the impact of soil compaction on root system architecture in tomato (Solanum lycopersicum) by X-ray micro-computed tomography. Ann. Bot. 2012, 110, 511–519. [Google Scholar] [CrossRef] [Green Version]
  104. Garbout, A.; Munkholm, L.J.; Hansen, S.B. Temporal dynamics for soil aggregates determined using X-ray CT scanning. Geoderma 2013, 204–205, 15–22. [Google Scholar] [CrossRef]
  105. Tracy, S.R.; Roberts, J.A.; Black, C.R.; McNeill, A.; Davidson, R.; Mooney, S.J. The X-factor: Visualizing undisturbed root architecture in soils using X-ray computed tomography. J. Exp. Bot. 2010, 61, 311–313. [Google Scholar] [CrossRef] [Green Version]
  106. Downie, H.; Holden, N.; Otten, W.; Spiers, A.J.; Valentine, T.A.; Dupuy, L.X. Transparent Soil for Imaging the Rhizosphere. PLoS ONE 2012, 7, e44276. [Google Scholar] [CrossRef] [PubMed]
  107. Mees, F.; Swennen, R.; Van Geet, M.; Jacobs, P. Applications of X-ray computed tomography in the geosciences. Geol. Soc. Lond. Spec. Publ. 2003, 215, 1–6. [Google Scholar] [CrossRef] [Green Version]
  108. Munkholm, L.J.; Heck, R.J.; Deen, B. Long-term rotation and tillage effects on soil structure and crop yield. Soil Tillage Res. 2013, 127, 85–91. [Google Scholar] [CrossRef]
  109. Metzner, R.; Eggert, A.; van Dusschoten, D.; Pflugfelder, D.; Gerth, S.; Schurr, U.; Uhlmann, N.; Jahnke, S. Direct comparison of MRI and X-ray CT technologies for 3D imaging of root systems in soil: Potential and challenges for root trait quantification. Plant Methods 2015, 11, 17. [Google Scholar] [CrossRef]
  110. Brunke, O.; Brockdorf, K.; Drews, S.; Müller, B.; Donath, T.; Herzen, J.; Beckmann, F. Comparison between X-ray tube-based and synchrotron radiation-based μCT. In Developments in X-ray Tomography VI; SPIE: Bellingham, WA, USA, 2008; Volume 7078, p. 70780U. [Google Scholar]
  111. Feeney, D.S.; Crawford, J.W.; Daniell, T.; Hallett, P.D.; Nunan, N.; Ritz, K.; Rivers, M.; Young, I.M. Three-dimensional Microorganization of the Soil–Root–Microbe System. Microb. Ecol. 2006, 52, 151–158. [Google Scholar] [CrossRef]
  112. Tumlinson, L.G.; Liu, H.; Silk, W.K.; Hopmans, J.W. Thermal Neutron Computed Tomography of Soil Water and Plant Roots. Soil Sci. Soc. Am. J. 2008, 72, 1234–1242. [Google Scholar] [CrossRef] [Green Version]
  113. Meyer, M.; Buchmann, C.; Schaumann, G.E. Determination of quantitative pore-size distribution of soils with 1H NMR relaxometry. Eur. J. Soil Sci. 2018, 69, 393–406. [Google Scholar] [CrossRef]
  114. Luster, J.; Göttlein, A.; Nowack, B.; Sarret, G. Sampling, defining, characterising and modeling the rhizosphere—The soil science tool box. Plant Soil 2009, 321, 457–482. [Google Scholar] [CrossRef]
  115. Guo, L.; Chen, J.; Cui, X.; Fan, B.; Lin, H. Application of ground penetrating radar for coarse root detection and quantification: A review. Plant Soil 2013, 362, 1–23. [Google Scholar] [CrossRef] [Green Version]
  116. Borden, K.A.; Isaac, M.E.; Thevathasan, N.V.; Gordon, A.M.; Thomas, S.C. Estimating coarse root biomass with ground penetrating radar in a tree-based intercropping system. Agrofor. Syst. 2014, 88, 657–669. [Google Scholar] [CrossRef]
  117. Butnor, J.R.; Barton, C.; Day, F.P.; Johnsen, K.H.; Mucciardi, A.N.; Schroeder, R.; Stover, D.B. Using Ground-Penetrating Radar to Detect Tree Roots and Estimate Biomass. In Measuring Roots: An Updated Approach; Mancuso, S., Ed.; Springer: Berlin/Heidelberg, Germany, 2012; pp. 213–245. ISBN 978-3-642-22067-8. [Google Scholar]
  118. Samuelson, L.J.; Butnor, J.; Maier, C.; Stokes, T.A.; Johnsen, K.; Kane, M. Growth and Physiology of Loblolly Pine in Response to Long-Term Resource Management: Defining Growth Potential in the Southern United States. Can. J. For. Res. 2008, 38, 721–732. [Google Scholar] [CrossRef]
  119. Huisman, J.A.; Snepvangers, J.J.J.C.; Bouten, W.; Heuvelink, G.B.M. Mapping spatial variation in surface soil water content: Comparison of ground-penetrating radar and time domain reflectometry. J. Hydrol. 2002, 269, 194–207. [Google Scholar] [CrossRef]
  120. Huisman, J.A.; Snepvangers, J.J.J.C.; Bouten, W.; Heuvelink, G.B.M. Monitoring Temporal Development of Spatial Soil Water Content Variation: Comparison of Ground Penetrating Radar and Time Domain Reflectometry. Vadose Zone J. 2003, 2, 519–529. [Google Scholar] [CrossRef]
  121. Piñeros, M.A.; Larson, B.G.; Shaff, J.E.; Schneider, D.J.; Falcão, A.X.; Yuan, L.; Clark, R.T.; Craft, E.J.; Davis, T.W.; Pradier, P.-L.; et al. Evolving technologies for growing, imaging and analyzing 3D root system architecture of crop plants. J. Integr. Plant Biol. 2016, 58, 230–241. [Google Scholar] [CrossRef] [PubMed]
  122. Ball, B.C.; Batey, T.; Munkholm, L.J. Field assessment of soil structural quality—A development of the Peerlkamp test. Soil Use Manag. 2007, 23, 329–337. [Google Scholar] [CrossRef]
  123. Liesch, A.M.; Krueger, E.S.; Ochsner, T.E. Soil Structure and Physical Properties under Rye-Corn Silage Double-Cropping Systems. Soil Sci. Soc. Am. J. 2011, 75, 1307–1314. [Google Scholar] [CrossRef]
Table
Table 1. Impact of cover crops on soil physical properties (ρb—bulk density; PSD—pore size distribution; Ksat—saturated hydraulic conductivity) measured using various techniques for indirect estimation of root-soil interactions.
Table 1. Impact of cover crops on soil physical properties (ρb—bulk density; PSD—pore size distribution; Ksat—saturated hydraulic conductivity) measured using various techniques for indirect estimation of root-soil interactions.
MeasurementsCover Crop(s)EffectRef.
Air permeabilityBrassica napus cv. ‘Essex’; Raphanus sativus var.; longipinnatus cv. ‘Daikon’; Secale cereal cv. ‘Wheeler’;Yes (+)[77]
Secale cerealeYes (+)[46]
Bulk density (ρb) by ProctorCrotalaria juncea; Glycine max; Vicia villosaYes (−)[64]
Bulk density (ρb)Brassica napus cv. ‘Essex’; Raphanus sativus var.; longipinnatus cv. ‘Daikon’; Secale cereal cv. ‘Wheeler’No[13,77]
Secale cerealeNo[70,74]
Vicia sativa; Vicia villosa; Secale cerealeYes (−)[76,78]
Yes (−)[46]
Triticum aestivum; Vicia villosaNo[79]
Penetration resistance (cone penetrometer)Secale cerealeYes (−)[76,80]
No[13,64,77]
Penetration resistance (micropenetrometer)Avena sativa; Vicia sativaYes (−)[81]
Wet aggregate stabilitySinapis alba; Phacelia tanacetifolia; Raphanus sativus var.; Oleiformis; Trifolium alexandriumYes (+)[63]
Hordeum vulgare cv. ‘Virden’; Lolium multiflorum cv.; ‘Aubade’; Secale cereale cv. ‘Danko’Yes (+)[82]
Yes (+)[46,64,70,76,83]
Dry aggregate size distribution Yes (+/−)[20]
Organic carbonSecale cereale; Trifolium incarnatum; Vicia villosaYes (+)[20]
Yes (+)[2,3,7,8,17]
No[70]
Water retention Yes (+)[76,78]
No[70,74,77]
In situ field capacity; Water retentionBrachiaria ruziziensis; Centrosema pubescens; Cynodon nlemfuensis; Paspalum notatum; Psophocarpus palustris; Pueraria phaseoloides; Stizolobium deeringianum; Stylosanthes guianensisYes (+)[70]
PSD via infiltrationSecale cereale
Sinapis alba		Yes (+)[10]
PSD via capillary rise Yes (+)[74]
PSD via water desorption Yes (+)[76]
No[79]
Mean Pore radiusFestuca rubra; Lotus corniculatus; Medicago sativaYes (+/−)[84]
Resin-impregnated image analysis Yes (−)[84]
Least-limiting water range Yes (+)[77]
Constant head Ksat No[64,76,79]
Constant/falling head Ksat No[74]
Single-ring infiltration Yes (+)[74]
Double-ring infiltration Yes (+)[64,85]
Tension infiltration No[10]
Disc infiltration Yes (+/−)[84]
Infiltration (neutron moisture meter; time domain reflectometry) Trifolium alexandriumYes (+)[86]
Simulated rainfall infiltrationAvena sativa; Secale cerealeYes (+)[87]
Yes (+)[81]
Sprinkle infiltrationPisum sativum var. arvense; Trifolium alexandrium; Trifolium incarnatum; Triticum aestivum; Vicia villosaYes (+)[83]
Marriot infiltration Yes (+)[46]
Table
Table 2. Measurement techniques used in cover crop research for directly quantifying root-soil interactions (ρb—bulk density; PSD—pore size distribution).
Table 2. Measurement techniques used in cover crop research for directly quantifying root-soil interactions (ρb—bulk density; PSD—pore size distribution).
Cover CropsMeasurementsInstrumentationRef.
Fagopyrum esculentum; Lathyrus sativus; Linum usitatissimum; Melilotus officinalis; Phacelia tanacetifolia; Raphanus sativus var. Oleiformis; Secale cereale; Sinapis alba; Trifolium alexandrinum; Trifolium incarnatum; Vicia sativa; Vicia villosaMethylene staining; root scanning; PSD via tension infiltration; Root sampling (soil core); Water retention via tension infiltrationWinRhizo 4.1 (Regent Instruments, Quebec, QC, Canada)[10]
Raphanus sativus var.; longipinnatus cv. ‘Daikon’; Brassica napus cv. ‘Essex’; Secale cereale cv. ‘Wheeler’Root penetration (core-break); Penetration resistance (cone pene-trometer); ρb [12]
Secale cereale; Raphanus sativus cv. ‘Daikon’; Raphanus sativus; Brassica rapaMinirhizotron, length of 1 m, outside diameter of 50.8 mm, 45° angle, 12 mm image intervals.
Penetration resistance (cone penetrometer); Particle size analysis (hydrometer); ρb		XYZ123 (Bartz Technology, Santa Barbara, CA, USA)[14]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ogilvie, C.M.; Ashiq, W.; Vasava, H.B.; Biswas, A. Quantifying Root-Soil Interactions in Cover Crop Systems: A Review. Agriculture 2021, 11, 218. https://doi.org/10.3390/agriculture11030218

AMA Style

Ogilvie CM, Ashiq W, Vasava HB, Biswas A. Quantifying Root-Soil Interactions in Cover Crop Systems: A Review. Agriculture. 2021; 11(3):218. https://doi.org/10.3390/agriculture11030218

Chicago/Turabian Style

Ogilvie, Cameron M., Waqar Ashiq, Hiteshkumar B. Vasava, and Asim Biswas. 2021. "Quantifying Root-Soil Interactions in Cover Crop Systems: A Review" Agriculture 11, no. 3: 218. https://doi.org/10.3390/agriculture11030218

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop