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Article

Mechanical Resistance to Penetration for Improved Diagnosis of Soil Compaction at Grazing and Forest Sites

by
Luis Eduardo Akiyoshi Sanches Suzuki
1,*,
Dalvan José Reinert
2,
Clenio Nailto Pillon
3 and
José Miguel Reichert
2,4
1
Center of Technological Development, Federal University of Pelotas (UFPel), Pelotas 96010-610, RS, Brazil
2
Soils Department, Federal University of Santa Maria (UFSM), Santa Maria 97105-9000, RS, Brazil
3
Embrapa Clima Temperado, Pelotas 96001-970, RS, Brazil
4
Nuclear Energy Department, Federal University of Pernambuco, Recife 50670-901, PE, Brazil
*
Author to whom correspondence should be addressed.
Forests 2024, 15(8), 1369; https://doi.org/10.3390/f15081369
Submission received: 12 July 2024 / Revised: 1 August 2024 / Accepted: 2 August 2024 / Published: 6 August 2024
(This article belongs to the Special Issue Impacts of Forest Operations on the Sustainable Management of Forest Soils)
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Abstract

:
Penetrometers and penetrographers are widely used to measure soil resistance to penetration, but the results are associated with other soil properties (such as bulk density, water content, and particle size distribution). Thus, for an adequate interpretation of results, site-specific studies are necessary to identify which properties are more related to soil resistance. We aimed to measure the resistance to penetration of a Typic Paleudalf under distinct soil uses and to identify soil properties that influence soil resistance. The soil uses in this study included anthropized forest (composed of tree and shrub species), pasture (5-year-old pasture), Eucalyptus 20 (a 20-year-old Eucalyptus saligna stand), and Eucalyptus 4.5 (a 4.5-year-old Eucalyptus saligna under the second rotation). Soil resistance to penetration was measured with an impact penetrometer, and the data were correlated with other physical and mechanical properties of soil, such as the particle size, soil moisture, air permeability, saturated hydraulic conductivity, porosity, bulk density, precompression stress, and compressibility index. We observed that a resistance of 1.3 MPa matches with other soil property values corresponding to soil compaction, and values greater than 1.3 MPa were verified at depths of 0–8 cm for pasture and 8–30 cm for Eucalyptus 4.5. Analyzing all soil uses together, the correlation was significant (p < 0.05) with gravel (r = 0.34), silt (r = −0.32), clay (r = 0.26), gravimetric moisture (r = −0.27), macroporosity (r = 0.24), and soil bulk density at the end of the compressibility test (r = 0.27). The penetrometer is useful for evaluating the physical conditions of soil, but we highlight that soil resistance is influenced by factors such as particle size and soil moisture, as examples. We recommend using a set of soil properties for a better interpretation of penetration resistance data and to support decision-making regarding soil management.

1. Introduction

Measurement of soil resistance to penetration is widely adopted to quantify and characterize the resistance of soil to root growth and penetration [1,2]. Soil resistance is also related to other physical properties of soil, crop growth, and yield [2,3,4]. Furthermore, resistance to penetration has been used to construct an environmental fragility index [5] and provides valuable information on soil strength parameters concerning the appropriate choice of tillage tools and the intensity of soil cultivation [6]. Nonetheless, in a spatial variability study, no correlation was observed between soil resistance to penetration and other physical properties of soil, but in deeper layers, a negative correlation between resistance and soil moisture in a lowland area was observed [7]. This behavior shows the influence of soil moisture on the resistance and the need for site-specific studies to understand the soil resistance and its relationship with other properties.
Comparing three rotational pasture management systems, resistance measurement was more effective than bulk density in indicating alterations in soil compaction due to a larger number of resistance measurements than soil core sampling to determine bulk density (for bulk density, ten measurements were taken per experimental unit, considering each depth interval, and 30 for penetration resistance) [8]. Similarly, Prazeres et al. [9] verified that in a Humic Cambisol under no-tillage, with and without chiseling, soil resistance to penetration was a more responsive indicator for defining compaction than relative density. For different soils with eucalyptus (Planosol, Nitisol, and Regosol), using soil resistance to penetration and bulk density, or more intricate indicators such as the degree of compactness and least limiting water range, the results supporting tillage recommendations were the same regarding soil tillage [10].
To measure soil resistance, penetrometers and penetrographs are used in the field [2,10,11,12,13,14,15,16,17,18] or the laboratory [19,20]. These tools are practical, fast, and easy to use, providing an index of compaction or allowing for comparisons between different soil uses and management practices [21,22,23]. Given the practicality of penetrometers and penetrographs, studies are being conducted to reduce equipment costs and improvements [24,25], considering the wide use of soil resistance data in decision-making. For instance, Marques Filho et al. [24] developed a low-cost system that is 800% cheaper than the mean cost of equipment available in the market, enabling greater access to this equipment.
Soil resistance to penetration—despite being easy to determine—is a property that generally depends on moisture, bulk density, and texture [11,12,26,27,28,29,30,31], hindering data analysis and, thus, requiring care and knowledge for adequate interpretation. In this sense, our study seeks to identify the properties that influence soil resistance in a Typic Paleudalf subjected to different uses, assisting in decision-making regarding soil management.
Knowing the soil properties that directly influence soil resistance, mathematical models can be proposed based on various parameters: soil moisture and bulk density [26,30,32]; matric potential and bulk density [33,34]; clay content, soil moisture, and bulk density [14]; and other parameters, such as the S index (which is the slope at the inflection point of the soil water retention curve [35]). These models can be used to estimate soil resistance [36] or define a set of properties to be evaluated alongside soil resistance for the proper diagnosis of soil. Considering the alterations in soil properties caused by different soil uses, we aim to define the soil properties related to soil resistance that may be included in a mathematical model for specific conditions.
Site-specific studies are necessary to verify the soil properties that influence soil resistance; this is because soil variability (texture, mineralogy, organic matter, among others) may influence soil resistance differently, whereas different soil uses and management practices can affect soil resistance in different ways. Thus, in our study, we aimed to characterize the resistance to penetration of a Typic Paleudalf and to identify soil properties related to resistance to penetration. Our hypotheses are as follows: land uses influence soil resistance in different ways, and soil resistance is directly linked to other soil properties, such as bulk density, soil moisture, and particle size distribution. Our results can assist farmers, forest companies, extension workers, and other stakeholders in better interpreting soil resistance and decision-making regarding soil management.

2. Materials and Methods

The research site was located in the city of Butiá, Rio Grande do Sul State, in southern Brazil (Figure 1). The climate in this region is classified as “Cfa” (subtropical and humid, without drought, according to the Köppen climatic classification system). Considering the data from 1981 to 2020, the mean temperature varies from 9 to 19 °C, and the rainfall reaches a mean value of 124.37 mm per month [37].
The soil under study is classified as a Typic Paleudalf [38], Umbric Rhodic Acrisol [39], or “Argissolo Vermelho Distrófico” [40], the relief is classified as smooth undulated and undulating, and the bedrock is granite. Soil morphology characterization and detailed descriptions of the soil uses are presented in Suzuki et al. [41], and below (Table 1). The particle size distribution of soils is shown in Figure 2. The soil morphology was described by Raphael David dos Santos and Edésio Paulo Bortolas (personal communication).
The soil uses studied, described below, were situated in adjacent commercial areas, not randomized (Figure 1):
(1) Anthropized forest (A. Forest): Forest composed of tree and shrub species (height of approximately 4 m) used as shelter for cattle, especially in the driest periods.
(2) Pasture: 5-year-old pasture, consisting of Brachiaria brizantha (Brachiaria brizantha) intercropped with Pensacola (Paspalum lourai) and clover (Trifolium sp.). The pasture was seeded under conventional tillage (plowing and harrowing) in 2001. Before pasture, there was natural forest, pasture, and soybean in the sequence.
(3) Eucalyptus 20: A 20-year-old Eucalyptus saligna stand, with conventional tillage before planting. The stand was planted in 1986. Before the eucalyptus stand, pasture was present in the area.
(4) Eucalyptus 4.5: A 4.5-year-old clonal Eucalyptus saligna in a second rotation. The first plantation was in 1993, using soil strip tillage with a three-stem chisel. Harvesting of eucalyptus in the first standing, at 8.5 years old, was conducted manually with a chainsaw, and wood extraction was carried out with a Forwarder Valmet 890 with a load capacity of 18 Mg, transiting at random with several passes, reaching up to 16. The second planting of eucalyptus occurred between the rows in 2002. Before the first planting in 1993, the area was used for soybean and pasture.
Soil core samples (undisturbed) were collected using metal rings that were 2.5 cm in height and 6.1 cm in diameter, in September 2006. This occurred in three trenches per soil use, and within each trench, two samples were taken per layer (2.5–5.0, 10–12.5, and 20–22.5 cm), totaling six replicates per soil layer and use. The soil cores were saturated by capillarity and, in the laboratory, the saturated hydraulic conductivity was quantified using a constant-load permeameter [42]. Afterward, the soil cores were kept saturated until they were positioned on a tension table at 6 kPa to determine macroporosity (pore diameter larger than 0.05 mm) and microporosity (pore diameter smaller than 0.05 mm). After two days, they were oven-dried at 105 °C for two days. Then, bulk density (BDi), total porosity (TP), macroporosity (macro), and microporosity (micro) were calculated [43].
Before drying at 105 °C, the soil cores were re-saturated and subjected to a 33 kPa-tension using Richards pressure chambers [44], followed by an air permeability evaluation. Afterward, the soil cores equilibrated at 33 kPa-tension were subjected to a uniaxial compression test; this involved the application of successive static loads for five minutes at 12.5, 25, 50, 100, 200, 400, 800, and 1600 kPa in the Terraload model S−450 (Durham Geo-Enterprises) consolidator. The total soil deformation was measured; this loading time reached more than 99% of soil deformation. After the compressibility test, the soil cores were dried at 105 °C. The vertical displacement measured by the consolidometer after the application of each load was used to determine the soil deformation (Def) at the end of the test. The compressibility index (CI) and the precompression stress (PCS) were estimated using Casagrande’s method [45].
Soil resistance to penetration was evaluated with an impact penetrometer [46] in the field, up to a depth of 30 cm, considering two replicates per plot, totaling six replicates per soil use. The procedure consists of releasing a 4 kg weight from a height of 40 cm. The number of impacts needed for the stem to penetrate each 1 cm into the soil is recorded.
Soil resistance was calculated by Equation (1):
F = 5.6 + 6.89 × N
where F is the soil resistance (kgf cm−2 × 0.098 = MPa), N is the number of impacts ÷ deepening (cm).
In the eucalyptus stand, soil resistance was determined in the inter-row (between four trees). We used the mean resistance to penetration from soil layers 2.5–5.0, 10–12.5, and 20–22.5 cm to correlate with other physical and mechanical properties of soil measured in the soil’s core samples (undisturbed). Soil resistance data from a depth of 0 to 30 cm were used to plot the soil resistance profile. The core soil samples (undisturbed) collected in the soil layers 2.5–5.0, 10–12.5, and 20–22.5 cm were also used to measure the volumetric soil moisture during the resistance to penetration evaluation.

Data Analysis

Because the sites under study are commercial areas, a randomized experimental design was not used in the field. A normality test was applied to the soil resistance to penetration data to confirm the normal distribution of the data. Then, ANOVA was performed and the least significant difference (LSD) was used to compare the soil resistance to penetration across different soil uses. Descriptive statistics (mean, maximum, and minimum values, standard deviation (SD), and coefficient of variation (CV)) for some physical and mechanical soil properties are presented herein. The results were analyzed using Pearson’s correlation through the statistical program SAS [47]; stepwise regression (forward) analysis was also conducted. The coefficient of variation was interpreted based on work by Pimentel-Gomes and Garcia [48], using the following ranges: low (smaller than 10%), medium (between 10 and 20%), high (between 20 and 30%), and very high (larger than 30%), while the correlation coefficients were interpreted using the ranges indicated by Mukaka [49] and Schober et al. [50] (Table 2).

3. Results

Soil resistance to penetration varied with soil use and depth (Figure 3). Generally, soil resistance increased in deeper layers. From a depth of 10 cm, soil resistance followed the following sequence: Eucalyptus 20 > Eucalyptus 4.5 > pasture > anthropized forest. In the topsoil (0–8 cm), the highest soil resistance was observed in the pasture. The greatest resistance to penetration was observed in the 0–8 cm layer for pasture and the 8–30 cm layer for Eucalyptus 4.5.
When measuring soil resistance in the field, the volumetric moisture of the soil was similar to that at a 33 kPa-tension (field capacity) obtained in the laboratory (Figure 3). In Eucalyptus 20, soil moisture and resistance to penetration increased in deeper layers, while for other uses (anthropized forest, pasture, and Eucalyptus 4.5), soil moisture and resistance to penetration presented slight variations (Figure 3).
In general, the coefficient of variation, considering the range proposed by Pimentel-Gomes and Garcia [48], was broad, ranging from low to very high (Table 3 and Table 4). The higher the coefficient of variation, the higher the data variability.
Pearson’s correlation did not follow the same pattern among the studied soil uses, with different physical and mechanical properties influencing the soil resistance to penetration (Table 5). The compressive properties (precompression stress and compressibility index) did not correlate with soil resistance. Meanwhile, soil resistance correlated with physical and mechanical properties for the anthropized forest, Eucalyptus 20, and Eucalyptus 4.5, while for pasture, no correlation was observed (Table 6). Considering the same soil type, particle size distribution, and site, the different variables correlating or not with soil resistance may arise from soil use, differently influencing the soil properties.
In general, the correlation was moderate (r = 0.40–0.69) following the interpretation based on Mukaka [49] and Schober et al. [50], but significant at a 5% probability. In general, soil particle size (gravel, sand, silt, and clay) influenced soil resistance in the anthropized forest (gravel (r = 0.69 **), total sand (r = −0.60 **), coarse sand (r = −0.55 *), silt (r = −0.55 *), clay (r = 0.66 **), Eucalyptus 4.5 (gravel (r = 0.62 **), total sand (r = −0.67 **), coarse (r = −0.63 **), fine sand (r = −0.63 **), silt (r = −0.55 *), and clay (r = 0.66 **). The structural or porous system variables (air permeability—AP (r = −0.67 **), macroporosity—macro (r = 0.61 **), total porosity—TP (r = 0.55 *), and soil deformation—Def (r = 0.79 *)) were correlated with soil resistance in Eucalyptus 20 (Table 5; Figure 4).
When all soil uses were analyzed, the correlation was weak (r = 0.10–0.39) but significant at a 5% probability. The influence of the soil particle size (gravel, silt, and clay) and structural or porous system variables (gravimetric moisture—ϴg, macroporosity—macro, and bulk density-BDf) influenced the soil resistance to penetration (Table 5; Figure 4). Considering the soil particle size influence, an increase in gravel (r = 0.34 **) and clay content (r = 0.26 *) and a decrease in silt (r = −0.32 **) increased soil resistance, while the influence of the soil structure showed a decrease in gravimetric moisture (r = −0.27 *), and an increase in macroporosity (r = 0.24 *) and bulk density (at the end of the compression test) (r = 0.27 *) increased soil resistance (Table 5; Figure 4).
We draw attention to the fact that the number of samples used in Pearson’s correlation analysis was 18 for each soil use (three trenches per soil use × two samples per layer (2.5–5.0, 10–12.5, and 20–22.5 cm), while the total number of samples considering all soil uses in the analysis was 72 (18 samples for each soil use x 4 soil uses). This explains the lower but significant correlation coefficient (p < 0.05), considering all soil uses, and the higher but non-significant correlation coefficient when considering each soil use in the analysis.
The stepwise regression procedure, using the entire dataset of soil uses, resulted in seven steps, including gravimetric moisture (ϴg), gravel, and fine sand in the final model (RP = 2.47436 − 3.69516 ϴg + 0.00034709 gravel—0.00513 fine sand; R2 = 0.53 **) (Table 6). This result shows the significant influence of granulometric components (gravel and fine sand) and moisture on the estimation of soil resistance under the conditions of our study.

4. Discussion

Soil resistance to penetration and moisture varied with soil use and depth, probably due to the distinct straw amount and ground cover of the soil, water use by the plants, and the evapotranspiration process. The volumetric moisture of soil uses, when measuring soil resistance in the field, was similar to the 33 kPa-tension (field capacity) obtained in the laboratory, in agreement with the literature, which states that the soil resistance using impact penetrometer should be measured at soil moisture between field capacity and permanent wilt pointing, since for very wet soils there is no difference in the resistance values, while for drier soils, the evaluation is time-consuming and difficult due to high mechanical resistance [51].
When measuring resistance to penetration in drier soil, the resistance values will increase; but this high resistance does not necessarily indicate compaction [52], while high moisture hinders the compaction detection for some soil types [52]. For example, increasing the soil moisture of a Rhodic Eutrudox decreased the soil resistance differences among soil compaction treatments, and these differences practically disappeared when moisture was close to field capacity [29]. Regarding soil moisture at field capacity, for the same bulk density, an increase in clay content also increased the soil resistance [27].
In Eucalyptus 20, soil moisture and resistance to penetration increased in deeper layers, while for other uses (anthropized forest, pasture, and Eucalyptus 4.5), soil moisture and resistance to penetration presented slight variations. But in an integrated crop-livestock grazing system, increasing moisture in deeper soil layers reduced penetration resistance [22].
Corroborating with our results regarding the larger resistance to penetration in the 0–8 cm layer for pasture and 8–30 cm for Eucalyptus 4.5, studies using other physical properties of soil for these same soil uses and sites showed soil compaction until 40 cm for Eucalyptus 4.5 and 10 cm for pasture. This response is due to, respectively, eucalyptus harvesting in first-rotation and animal trampling. These factors affect bulk density, porosity [53], water conductivity, the diameter of aggregates, water retention, pore size distribution, the S index [54], and the degree of compactness [55]. Thus, soil resistance to penetration is represented in a similar way to soil compaction, according to the soil uses.
Soil uses influence soil resistance differently in terms of depth and range of values; these differences may be associated with the type of root (eucalyptus and pasture, for example), straw deposition, plant architecture, animal trampling (pasture), machinery traffic (eucalyptus), tillage, the time required for soil reconsolidation after tillage, and other specific practices applied in each soil use case. A combination of variables (plants, machinery, and others) differently influences soil resistance along the soil profile.
Understanding the soil compaction depth and intensity is important for soil management to reduce or eliminate soil compaction through mechanical (tillage) or biological strategies (plants). Soil compaction in deeper layers is more difficult to ameliorate. The negative influence of machinery traffic during harvesting operations on resistance in the 0–5 cm layer of an Oxisol was mitigated for the eucalypt straw on the soil surface [23], similar to our results, with smaller resistance to penetration in the topsoil (0–8 cm) in Eucalyptus 4.5, probably due to straw on the soil surface. Moreover, two forwarder passes were responsible for the significant increase in soil resistance and bulk density in a clayey Oxisol [56].
While we observed the highest soil resistance for pasture use in the topsoil (0–8 cm), another study verified that resistance to penetration was significantly affected by rotational pasture management to a depth of 25.5 cm [8]. In five farms practicing crop and livestock integration, high soil resistance in the topsoil (0–10 cm) of Oxisol was due to cattle trampling [57], corroborating our results of higher soil resistance in the topsoil (0–8 cm) of grazing pasture. In a soybean crop and livestock integration system, soil resistance reached the highest values in the 0–15 cm soil layer in Oxisol [58]. Soil resistance to penetration was approximately 56.13% higher under cattle and horse livestock grazing than in livestock-excluded areas for five years [59].
A study showed that increasing the resistance to penetration decreased the root growth of Panicum maximum, while the highest root growth was observed with soil resistance smaller than 1 MPa [60]. Despite our punctual or unidimensional soil resistance to penetration measurements, some authors have shown variability in soil resistance in the “x” and “y” axes of the soil profiles (bi-dimensional), both in planted forests [13] and annual crops [2]. At times, soil resistance layers are not continuous, with points of smaller resistance that allow the roots to penetrate these points and grow deeper [2].
Despite the general dependence of soil resistance on moisture, bulk density, and particle size distribution, research on critical values of resistance for root growth has generally focused on annual crops, while studies using pastures and eucalyptus are scarce. Thus, alternatively, we use the critical resistance values for annual crops as references for pasture and eucalyptus crops. For instance, resistance values of 1.5 to 2 MPa have been found to restrict soybean and edible black bean root growth in silty clay loam, silty clay, and sandy loam Ultisols; from 1 to 1.5 MPa in a loam Ultisol; and from 2 to 3.5 MPa in a clay Oxisol [2]. For soybean and wheat grain yields in clayey Rhodic Eutrudox, 3.5 MPa is considered critical resistance for continuous no-tillage; 3 MPa for minimum tillage, comprising annual chiseling or chiseling every three years; and 2 MPa for conventional tillage with annual tilling [4]. The critical soil resistance for bean yield is 1.7 MPa, while soybean height is negatively influenced, starting at 1.9 MPa [4].
In general, soil resistance values below 2 MPa are considered non-critical for plant growth [61]. For instance, approximately 68% of roots (Italian alder, Japanese larch, Corsican pine, and birch with 5-yr tree growth) occur in soil (sandy loam with stoniness) with resistance to penetration smaller than 2 MPa, and 90% with resistance smaller than 3 MPa [62]. Another study showed that the lengths of primary and lateral roots of eucalypt seedling roots decreased by 71 and 31% with an increase in resistance to penetration from 0.4 to 4.2 MPa. By increasing the soil strength, there was an increase in the length of lateral roots and the diameters of primary and lateral root tips [63]. Canarache [27] suggested soil resistance values that limit root growth (no specific crop) as follows: not limiting (≤2.5 MPa), somewhat limiting (2.6 to 10 MPa), and no root growth possible (>10.1 MPa).
Understanding the critical limits of soil resistance is important for managing soil to enhance plant growth and crop yield [64]. However, as stated above, there is a broad variation in critical values reported in the literature. In our study, considering the highest soil resistance in pasture (0–8 cm) and Eucalyptus 4.5 (8–30 cm), along with results from other soil properties evaluated for these same soil uses and sites, like bulk density and porosity [53], water conductivity, the mean weight-diameter of aggregates, water retention curve, pore size distribution, S parameter [54], and degree of compactness [55], which indicated soil compaction up to 40 cm for Eucalyptus 4.5, and 10 cm for pasture, we identified 1.3 MPa as a resistance level that negatively affects these soil properties, although we did not measure yield or plant growth variables for pasture and eucalyptus. This means that the critical soil resistance of 1.3 MPa in our study was chosen because this value matches the same depth of the critical values of other soil properties. From this value, the resistance practically does not change in deeper soil layers in both pasture and Eucalyptus 4.5.
Despite our results showing no correlation between resistance to penetration and precompression stress, some authors have observed a significant relationship [12,65,66,67,68,69]. Constantini [11], Imhoff et al. [70], and Assis et al. [71] also verified a negative correlation between resistance to penetration and soil moisture. Different from our study, Constantini [11] and Imhoff et al. [70] observed a positive correlation with bulk density, corroborating with Halde et al. [8], who found a significant correlation between bulk density and soil resistance in intensive, semi-intensive, and extensive rotational pasture management systems. Halde et al. [8] verified that the rate of change in resistance to penetration with soil moisture was greater for higher bulk density, while resistance to penetration with bulk density was larger for lower soil moisture.
The final model (RP = 2.47436 − 3.69516 ϴg + 0.00034709 gravel − 0.00513 fine sand; R2 = 0.53 **), obtained through the stepwise regression procedure using the entire dataset of soil uses, shows a significant influence of the granulometric (gravel and fine sand) and moisture on the estimation of soil resistance under the conditions of our study. Other authors also found soil resistance to be dependent on moisture [11,12,26,27,28,29,30,72], bulk density [11,26,27,28,29,30,31], and particle size distribution [27,28,31]. Busscher [26] proposed a mathematical function to estimate soil resistance based on volumetric moisture and bulk density, and it has been largely used [73,74,75,76,77]. Furthermore, Mirreh and Ketcheson [33] and Imhoff et al. [70] elaborated on three-dimensional plots of soil resistance with bulk density and matric pressure/moisture, allowing the prediction of soil resistance at specific soil conditions. For example, the highest penetration resistance tended to occur at a higher density and lower moisture, whereas the lowest resistance to penetration tended to occur at lower soil bulk density and higher moisture [77].
Jiang et al. [14] observed a high to low influence of each single variable on soil resistance was bulk density > clay content > soil moisture, making it possible to estimate soil resistance through these variables. While other studies have used seven topsoils (0 to 15 cm), stepwise regression identified bulk density, gravimetric soil moisture, texture, clay mineralogy, and organic matter as significant properties to estimate resistance to penetration, while bulk density and gravimetric soil moisture contributed to 84% of the model [20]. Despite clay content being absent in the final equation obtained stepwise, in studies on soil resistance to penetration, clay content should be considered because it controls water content, which can reduce soil resistance [78].
Modeling the resistance to penetration of an Oxisol, following changes in moisture and bulk density, the lower soil moisture increased the variation of resistance, while for small variations in bulk density, the soil resistance varied according to moisture [32]. This relationship between soil resistance and moisture depends on bulk density [19]. In our study, despite soil moisture being included in the stepwise regression model, bulk density was not correlated to soil resistance and it was not included in the model, indicating its lesser influence in our study, which may be associated with soil type, particle size distribution, and other factors.
Although using penetrometers and penetrographs is easy, fast, economical, and practical, the recommendation is not to use only one soil property when analyzing the soil, but a set of properties such as bulk density, macroporosity, soil resistance to penetration, and degree of compactness. The latter has the advantage of being indirectly dependent on particle size distribution [55,79,80] or more complex variables, such as the least limiting water range [73], in a holistic and broad view of soil resistance.

5. Conclusions

Evaluating the soil resistance to penetration of a Typic Paleudalf under distinct soil uses (anthropized forest, pasture, eucalyptus aged 20 years and 4.5 years in their second rotation), in agreement with our first hypothesis, we observed greater resistance to penetration in the 0–8 cm layer for pasture and 8–30 cm layer for Eucalyptus 4.5. The resistance of 1.3 MPa negatively affects other soil properties.
When analyzing all soil uses together, Pearson’s correlation was weak but significant at a 5% probability with textural or granulometric variables (gravel, silt, and clay) and structural or porous system variables (gravimetric moisture, macroporosity, and bulk density at the end of the compressibility test). This outcome agrees with our second hypothesis that soil resistance is directly linked to other soil properties. Considering each soil use, the properties correlated with soil resistance are different, showing that soil use differently influences the soil properties.
Our results show the effectiveness of penetrometers in evaluating the physical/mechanical conditions of soil but also underscore the importance of using a set of soil properties, such as particle size and soil moisture, at the minimum, to properly support farmers, forest companies, extension workers, and other stakeholders in better interpreting penetration resistance data for making informed decisions about soil management options.

Author Contributions

Conceptualization, L.E.A.S.S. and D.J.R.; formal analysis, L.E.A.S.S., D.J.R., C.N.P. and J.M.R.; investigation, L.E.A.S.S., D.J.R. and C.N.P.; methodology, L.E.A.S.S., D.J.R., C.N.P. and J.M.R.; resources, D.J.R. and C.N.P.; writing—original draft, L.E.A.S.S. and D.J.R.; writing—review and editing, C.N.P. and J.M.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are available upon request from the authors.

Acknowledgments

We thank the technicians and student helpers from the “Embrapa Clima Temperado” of Pelotas-RS for their assistance in field and laboratory tasks. We also thank Capes for the scholarship during the first author’s doctorate (finance code 001), CNPq for the research productivity grants, and the landowners for allowing access to the field sites.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Forests 15 01369 g001
Figure 1. Chart of Brazil, Rio Grande do Sul in purple (a); chart of Rio Grande do Sul State with the city of Butiá in purple (b); an image from Google Earth (from 5 September 2005) with the soil uses in this study. Anthropized forest (A. Forest): forest composed of tree and shrub species; pasture: 5-year-old pasture; Eucalyptus 20: a 20-year-old Eucalyptus saligna stand; Eucalyptus 4.5: a 4.5-year-old clonal Eucalyptus saligna in the second rotation.
Figure 1. Chart of Brazil, Rio Grande do Sul in purple (a); chart of Rio Grande do Sul State with the city of Butiá in purple (b); an image from Google Earth (from 5 September 2005) with the soil uses in this study. Anthropized forest (A. Forest): forest composed of tree and shrub species; pasture: 5-year-old pasture; Eucalyptus 20: a 20-year-old Eucalyptus saligna stand; Eucalyptus 4.5: a 4.5-year-old clonal Eucalyptus saligna in the second rotation.
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Figure 2. Mean values of clay, silt, sand, and gravel for the soil use and layers.
Figure 2. Mean values of clay, silt, sand, and gravel for the soil use and layers.
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Figure 3. Soil resistance to penetration and volumetric moisture obtained in the field and the laboratory at a 33 kPa-tension for the different soil uses. The bars indicate the least significant difference (LSD); 33 kPa = soil moisture obtained in the laboratory at 33 kPa-tension.
Figure 3. Soil resistance to penetration and volumetric moisture obtained in the field and the laboratory at a 33 kPa-tension for the different soil uses. The bars indicate the least significant difference (LSD); 33 kPa = soil moisture obtained in the laboratory at 33 kPa-tension.
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Figure 4. Flowchart of the significant Pearson’s correlation between soil resistance to penetration and the physical and mechanical properties of soil, for the soil uses. The colored background denotes the interpretation of the correlation coefficient according to Mukaka [49] and Schober et al. [50]: 0.10–0.39: weak correlation; 0.40–0.69: moderate correlation; 0.70–0.89: strong correlation. The signs (+) and (−) indicate, respectively, positive and negative correlation coefficients.
Figure 4. Flowchart of the significant Pearson’s correlation between soil resistance to penetration and the physical and mechanical properties of soil, for the soil uses. The colored background denotes the interpretation of the correlation coefficient according to Mukaka [49] and Schober et al. [50]: 0.10–0.39: weak correlation; 0.40–0.69: moderate correlation; 0.70–0.89: strong correlation. The signs (+) and (−) indicate, respectively, positive and negative correlation coefficients.
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Table 1. Soil morphology characterization. Source: Suzuki et al. [41].
Table 1. Soil morphology characterization. Source: Suzuki et al. [41].
HorizonDescription
A1Depth of 0–16 cm; the Munsell color of the moist soil is 2.5 YR 3/3; textural class is sandy clay loam; structure is weak; size class ranges from fine to medium; type is subangular blocky; consistency of soil mass is hard (dry), friable (moist), very plastic (plasticity), and very sticky (stickiness); horizon boundary—topography is smooth and the distinctness is gradual.
ABDepth of 16–28 cm; the Munsell color of the moist soil is 2.5 YR 3/6; textural class is clay loam; structure is weak; size class ranges from fine to medium; type is subangular blocky; consistency of soil mass is hard (dry), friable (moist), very plastic (plasticity), and very sticky (stickiness); horizon boundary—topography is smooth and the distinctness is gradual.
Bt1Depth of 28–47 cm; the Munsell color of the moist soil is 2.5 YR 4/4; textural class is clay; the structure is weak; size class ranges from fine to medium; type is subangular blocky; consistency of soil mass is hard (dry), friable (moist), very plastic, and very sticky (stickiness); horizon boundary—topography is smooth and the distinctness is diffuse.
Bt2Depth of 47–76 cm; the Munsell color of the moist soil is 2.5 YR 4/5; textural class is clay with gravel; the structure is weak to moderate; size class is medium; type is subangular blocky; consistency of soil mass is hard (dry), friable (moist), plastic (plasticity), and sticky (stickiness); horizon boundary—topography is wavy and the distinctness is gradual.
Bt3Depth of 76–88 cm; the Munsell color of the moist soil is 2.5 YR 4/6; textural class is gravel clay; the structure is weak/moderate; size class is medium; type is subangular blocky; consistency of soil mass is hard (dry), friable (moist), plastic (plasticity), and sticky (stickiness); horizon boundary—topography is wavy and the distinctness is gradual.
BCDepth of 88–120 cm; the Munsell color of the moist soil is 2.5 YR 4/6; mottle classification—abundance is common, size is fine, and contrast is prominent. The Munsell color of the moist mottles is 10 YR 6/6; the textural class is gravelly clay loam; the structure is weak; the size class ranges from fine to medium; the type is subangular blocky; the consistency of soil mass is very hard (dry), firm (moist), plastic (plasticity), and sticky (stickiness); horizon boundary—topography is wavy and the distinctness is clear.
CDepth of 120–160 cm; the Munsell colors of moist mottles are 2.5 YR 4/6, 10 YR 6/6, 10 YR 6/6, and 2.5 YR 5/6; textural class is silty clay loam; consistency of soil mass is very hard (dry), friable (moist), plastic (plasticity), and sticky (stickiness).
Table 2. Interpretation of the correlation coefficient based on work by Mukaka [49] and Schober et al. [50].
Table 2. Interpretation of the correlation coefficient based on work by Mukaka [49] and Schober et al. [50].
Correlation CoefficientInterpretation
0.00–0.10Negligible correlation
0.10–0.39Weak correlation
0.40–0.69Moderate correlation
0.70–0.89Strong correlation
0.90–1.00Very strong correlation
Table 3. Mean, maximum, minimum, standard deviation (SD), and coefficient of variation (CV, %) of some physical and mechanical soil properties for anthropized forest (A. Forest) and pasture.
Table 3. Mean, maximum, minimum, standard deviation (SD), and coefficient of variation (CV, %) of some physical and mechanical soil properties for anthropized forest (A. Forest) and pasture.
VariableMeanMaximumMinimumSDCV
A. Forest
θv, m3 m−30.3200.3630.2900.026.39
θg, kg kg−10.2500.2670.2340.014.21
AP, mm h−124.2081.050.0023.1395.59
KθS, mm h−117.1290.680.0026.53154.91
Macro, m3 m−3θ0.0660.1250.0150.0342.46
Micro, m3 m−30.3500.3900.3130.025.99
TP, m3 m−30.4160.4430.3980.023.97
BDi, Mg m−31.281.431.200.064.51
BDf, Mg m−31.701.771.600.053.02
Def, mm0.620.770.380.0914.18
RP, MPa0.730.890.630.078.98
PCS, kPa45.0066.4029.4011.2324.95
CI0.290.400.160.0518.30
Pasture
θv, m3 m−30.3160.3370.2620.025.11
θg, kg kg−10.2370.2600.2240.014.64
AP, mm h−119.1050.547.1211.3059.18
KθS, mm h−114.38121.451.3727.69192.58
Macro, m3 m−30.0640.1630.0360.0343.59
Micro, m3 m−30.3520.3750.2860.025.68
TP, m3 m−30.4160.4480.3960.013.77
BDi, Mg m−31.341.421.130.075.08
BDf, Mg m−31.711.771.640.031.99
Def, mm0.550.870.450.0917.13
RP, MPa0.921.220.740.1314.39
PCS, kPa37.7959.9021.3012.2432.39
CI0.230.390.180.0521.18
θv and θg = respectively, volumetric and gravimetric moisture during the soil resistance to a penetration test in the field; AP = air permeability; KθS = saturated hydraulic conductivity; macro = macroporosity−pore diameter larger than 0.05 mm; micro = microporosity−pore diameter smaller than 0.05 mm; TP = total porosity; BDi and BDf = respectively, bulk density before and after the uniaxial compression test; Def = soil deformation at the end of the uniaxial compression test; RP = soil resistance to penetration; PCS = precompression stress; CI = compressibility index.
Table 4. Mean, maximum, minimum, standard deviation (SD), and coefficient of variation (CV, %) of some physical and mechanical soil properties for Eucalyptus 20 and 4.5.
Table 4. Mean, maximum, minimum, standard deviation (SD), and coefficient of variation (CV, %) of some physical and mechanical soil properties for Eucalyptus 20 and 4.5.
VariableMeanMaximumMinimumSDCV
Eucalyptus 20
θv, m3 m−30.2320.2920.1780.0314.99
θg, kg kg−10.2010.2210.1800.016.35
AP, mm h−1191.11413.8124.88124.4265.10
KθS, mm h−171.01161.140.0059.5883.90
Macro, m3 m−30.0620.1210.0020.0356.46
Micro, m3 m−30.2750.3500.2170.0516.99
TP, m3 m−30.3370.4510.2190.0721.17
BDi, Mg m−31.151.360.960.1311.12
BDf, Mg m−31.781.881.680.074.02
Def, mm0.740.870.520.1115.59
RP, MPa0.931.220.620.2425.94
PCS, kPa40.2570.6018.4013.9634.67
CI0.470.780.240.1429.30
Eucalyptus 4.5
θv, m3 m−30.2600.3020.2180.029.36
θg, kg kg−10.1770.2150.1520.029.50
AP, mm h−123.9559.413.5419.7882.58
KθS, mm h−112.3356.730.0015.59126.49
Macro, m3 m−30.0720.1360.0370.0339.01
Micro, m3 m−30.2990.3320.2760.014.96
TP, m3 m−30.3710.4220.3180.037.52
BDi, Mg m−31.471.641.330.096.38
BDf, Mg m−31.851.931.760.052.69
Def, mm0.510.670.380.0916.91
RP, MPa0.891.220.620.2021.92
PCS, kPa46.3965.4030.8010.7923.26
CI0.200.290.120.0524.27
θv and θg = respectively, volumetric and gravimetric moisture during the soil resistance to the penetration test in the field; AP = air permeability; KθS = saturated hydraulic conductivity; macro = macroporosity−pore diameter larger than 0.05 mm; micro = microporosity−pore diameter smaller than 0.05 mm; TP = total porosity; BDi and BDf = respectively, bulk density before and after the uniaxial compression test; Def = soil deformation at the end of the uniaxial compression test; RP = soil resistance to penetration; PCS = precompression stress; CI = compressibility index.
Table 5. Pearson’s correlation between soil resistance to penetration (RP) with the physical and mechanical properties of soil uses.
Table 5. Pearson’s correlation between soil resistance to penetration (RP) with the physical and mechanical properties of soil uses.
A. ForestPastureEucalyptus 20Eucalyptus 4.5All Uses
VariableRPRPRPRPRP
Gravel0.69 **0.05 ns0.37 ns0.62 **0.34 **
Total sand−0.60 **0.38 ns0.02 ns−0.67 **−0.19 ns
Coarse sand−0.55 *0.35 ns0.10 ns−0.63 **−0.16 ns
Fine sand−0.40 ns0.32 ns−0.09 ns−0.63 **−0.21 ns
Silt−0.55 *0.29 ns−0.36 ns−0.55 *−0.32 **
Clay0.66 **−0.41 ns0.03 ns0.66 **0.26 *
Silt + clay0.60 **−0.38 ns−0.02 ns0.67 **0.19 ns
θv0.15 ns−0.13 ns0.44 ns−0.23 ns−0.13 ns
θg−0.19 ns−0.24 ns−0.08 ns−0.10 ns−0.27 *
AP−0.23 ns0.16 ns−0.67 **0.31 ns−0.08 ns
KθS−0.34 ns−0.11 ns−0.35 ns0.40 ns−0.00 ns
Macro0.24 ns−0.09 ns0.61 **0.12 ns0.24 *
Micro−0.26 ns0.07 ns0.38 ns−0.00 ns−0.05 ns
TP0.08 ns−0.07 ns0.55 *0.12 ns0.10 ns
BDi0.37 ns0.14 ns0.42 ns−0.18 ns0.12 ns
BDf0.47 *0.12 ns0.22 ns−0.06 ns0.27 *
Def−0.03 ns−0.10 ns0.79 *0.23 ns0.20 ns
PCS−0.13 ns0.28 ns0.30 ns0.08 ns0.08 ns
CI−0.14 ns−0.07 ns−0.40 ns0.22 ns−0.06 ns
Gravel = particles of diameter between 2 and 20 mm; total sand = particles of diameter between 2 and 0.05 mm; coarse sand = particles of diameter between 2 and 0.2 mm; fine sand = particles of diameter between 0.2 and 0.05 mm; silt = particles of diameter between 0.05 and 0.002 mm; clay = particles of diameter smaller than 0.002 mm; θv and θg = respectively, volumetric and gravimetric moisture during the soil resistance to penetration test in the field; AP = air permeability; KθS = saturated hydraulic conductivity; macro = macroporosity−pores greater than 0.05 mm; micro = microporosity−pores smaller than 0.05 mm; TP = total porosity; BDi and BDf = respectively, bulk density before and after the uniaxial compression test; Def = soil deformation at the end of the uniaxial compression test; PCS = precompression stress; CI = compressibility index; RP = soil resistance to penetration; ns = no significance; * = significance at 5%; ** = significance at 1%. The colored background denotes the interpretation of the correlation coefficient according to Mukaka [49] and Schober et al. [50]: 0.10–0.39: weak correlation; 0.40–0.69: moderate correlation; 0.70–0.89: strong correlation.
Table 6. Results of the stepwise regression procedure using all datasets.
Table 6. Results of the stepwise regression procedure using all datasets.
StepModelR2Significance
1RP = 1.67372 − 0.00448 Silt0.181%
2RP = 2.04690 − 1.80565 ϴg − 0.00435 silt0.291%
3RP = 0.52711 − 3.90300 ϴg + 0.00132 silt + 0.00217 clay0.451%
4The silt was not significant in the step 3, then, the equation may be simplified:
RP = 0.83614 − 3.58215 ϴg + 0.00185 clay		0.441%
5RP = 0.81254 − 3.27152 ϴg + 0.00030606 gravel + 0.00171 clay0.501%
6RP = 1.99928 − 3.64842 ϴg + 0.00033108 gravel − 0.00371 fine sand + 0.00054539 clay0.531%
7The clay was not significant in the step 6, then, the equation may be simplified:
RP = 2.47436 − 3.69516 ϴg + 0.00034709 gravel − 0.00513 fine sand		0.531%
RP (soil resistance to penetration): MPa; gravel, fine sand, silt, clay: g kg−1; ϴg (gravimetric moisture): kg kg−1.
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Suzuki, L.E.A.S.; Reinert, D.J.; Pillon, C.N.; Reichert, J.M. Mechanical Resistance to Penetration for Improved Diagnosis of Soil Compaction at Grazing and Forest Sites. Forests 2024, 15, 1369. https://doi.org/10.3390/f15081369

AMA Style

Suzuki LEAS, Reinert DJ, Pillon CN, Reichert JM. Mechanical Resistance to Penetration for Improved Diagnosis of Soil Compaction at Grazing and Forest Sites. Forests. 2024; 15(8):1369. https://doi.org/10.3390/f15081369

Chicago/Turabian Style

Suzuki, Luis Eduardo Akiyoshi Sanches, Dalvan José Reinert, Clenio Nailto Pillon, and José Miguel Reichert. 2024. "Mechanical Resistance to Penetration for Improved Diagnosis of Soil Compaction at Grazing and Forest Sites" Forests 15, no. 8: 1369. https://doi.org/10.3390/f15081369

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