Estimating Sample Size of Soil Cone Index Profiles by Bootstrapping

ABSTRACT

Measurements of the soil cone index are widely used to assess soil resistance to root penetration (SR) and to monitor the soil compaction status of agricultural fields. However, soil sampling for SR estimation is a rather challenging task in view of the high spatial and temporal variability of the soil. This study proposed a bootstrapping method to determine the minimum sample size required to estimate the vertical profile of mean soil cone index (CI) values at different levels of precision and confidence. For this purpose, CI data from a Typic Argiudoll under no-tillage before and after chiseling was used. A total of 151 CI profiles were recorded before and after chiseling in a 3,200 m² (40 × 80 m) no-tillage area at sampling points distributed on a horizontal 5 × 5 m aligned grid and from the top layer to 0.40 m depth by in 0.02 m intervals. A modified bootstrap routine was developed to estimate the sampling distribution of the sample mean and medians of CI values per layer. The minimum sample size to estimate the vertical profile of mean CI values at different levels of precision and confidence was determined from data of the whole soil profile, including the autocorrelation of CI readings in the vertical direction. Tilling increased the variability of this measurement and thus the sampling efforts to achieve the same level of precision and confidence were different before and after the procedure. The standard errors of sample medians estimated by bootstrapping were higher than those corresponding to sample means. In addition, to achieve the same level of precision and confidence, the estimation of the vertical profile of mean CI values based on sample medians required more observations than based on sample means. This study shows that the viability of the bootstrap approach to determine the implications of soil variability on the sampling efforts required for an accurate estimation of the vertical distribution of resistance in soils under different managements.

sampling; soil resistance; bootstrap procedure; soil variability

INTRODUCTION

Soil mechanical resistance to root penetration (SR) is a key soil physical property that controls root expansion, and consequently modulates the crop oxygen and water uptake (Letey, 1985). Soil cone index (CI) measurements are widely used to assess SR at field and lab conditions as well (Rooney and Lowery, 2000). Periodic monitoring of the soil compaction status of agricultural fields through CI is an effective way to assess the effects of different management systems and cultural practices on soil structure and root growth and development (Tavares Filho and Ribon, 2008). However, soil sampling for the estimation of SR can be challenging, because of the high spatial and temporal intrinsic variability of this soil property, which often increases the required sampling effort (Castrignanò et al., 2002; Han et al., 2016). In addition, the extrinsic sources of variation such as anthropogenic activities, machinery traffic, and tillage systems can increase SR variability (Veronese Júnior et al., 2006; Usowicz and Lipiec, 2009). Thus, the question about how many observations are required for a reliable estimation of the vertical profile of mean CI values, i.e., the mean CI values per layer, is a key aspect of the sampling design for soil compaction monitoring.

In the design-based sampling approach, the population of values in the region of interest is regarded as unknown but fixed, and randomness is introduced by the sampling design. In this context, the sampling distribution of the estimator is used to assess the uncertainty of the estimated parameters, e.g. the population mean (Gruijter et al., 2006). This uncertainty can be reduced by increasing the sample size until the area is fully covered (Wang et al., 2012). The sample size needed to estimate the population mean of a soil property, depends on the soil variability and the level of precision required. The more variable a soil property, the higher the sampling efforts required to achieve a desired level of precision. Whereas the variability of the soil property depends on the intrinsic soil variability, the precision level is set according to the research objectives and available resources (McBratney and Webster, 1983).

Assuming that the data of the target soil property are independent and were drawn from a population with normal distribution, the minimum sample size needed to estimate the population mean can easily be calculated by the expression (Mulla and McBratney, 2000):

$n={\left(\frac{t_{1-\alpha /2}CV}{d}\right)}^2$ Eq. 1

where: n is the minimum number of soil samples (observations), t_1-α/2 the t statistic for the 1-α/2 level of confidence, CV is the coefficient of variation, and d is the acceptable margin of relative error.

However, this method is unsuited when observations are autocorrelated and the distribution of the population being sampled is non-normal or has an unknown form (Dane et al., 1986). A lack of normality and spatial dependence are commonly observed for soil properties (Warrick and Van Es, 2002). The classical way of computing the variance estimator may result in an underestimation of variance of the process, if there is significant spatial autocorrelation. To solve this problem, Cressie (1993) established an alternative expression to compute the variance controlling spatial autocorrelation. On the other hand, the skewed distributions commonly observed in soil properties may be corrected by applying transformations such as the logarithmic, and then analyzed by using parametric methods, provided the assumptions are met (Sokal and Rolhf, 2012). Alternatively, data may be analyzed on the original scale by using robust estimators such as the sample median, which is less affected by departures from normality (Gubiani et al., 2011). However, the lack of an analytical method to assess the precision of a sample median limits the estimation of minimum sampling for this estimator (Efron and Tibshirani, 1993).

Information about how many observations are needed to estimate the vertical profile of mean CI values of an area of interest is limited. For example, Tavares Filho and Ribon (2008) studied the effect of different tillage systems on the sample needs for the estimation of CI using an impact penetrometer and reported that at least 15-20 samples are needed to estimate CI at a precision of 10 %. Molin et al. (2012) compared three penetrometers and studied the CI variation through the profile in depth intervals. These authors concluded that at least 15 replications are necessary for the estimation of CI with a precision varying from 5 to 15 %, depending on the penetrometer used. These sampling recommendations are based on the decreasing relationship between sample size and standard error of the mean of the target soil layer. Although straightforward, this approach has two drawbacks: (1) data are analyzed independently per layer, ignoring the spatial structure of the CI measures in the vertical direction (Castrignanò et al., 2002); and (2) the effect of sample size on the estimation accuracy is assessed based on the information contained in only one random sampling (Efron and Tibshirani, 1993).

Bootstrapping is a computer-intensive statistical technique that allows the approximation of the true sampling distribution of a given statistic by means of resampling procedures by an empirical cumulative distribution function (Efron and Tibshirani, 1993). The resulting sampling distribution is assumed to be good enough for assessing the precision of an estimator and construct confidence intervals (Chernick, 2008). Based on this approach, Dane et al. (1986) proposed a modified bootstrap algorithm to estimate sample size for bulk density in a Typic Paleudult. To estimate highly positively skewed hydraulic properties of a Typic Hapludox, Melo-Filho et al. (2002) compared the sample needs approximated by the classical formula (Equation 1) and a modified bootstrap algorithm. In these studies, the bootstrap approach was applied to soil properties separately for each layer. The information provided by soil penetrometers is typically autocorrelated in the vertical direction, ensuring a modification of the algorithm to integrate the information of the whole layer explored by the penetrometer.

The objective of this research was to propose a bootstrapping-based method to determine the sample size required for the estimation of the vertical profile of mean CI values with different levels of precision and confidence. For this purpose, CI data from a Typic Argiudoll under no-tillage before and after chiseling were used.

MATERIALS AND METHODS

Study site

The study was carried out on a commercial farm in Aurelia (31° 29’ 8.50” S, 61° 27’ 25.88” W, elevation 78 m a.s.l., datum WGS84), Santa Fe province (Argentina). This region is characterized by a flat relief with deep and well-drained silty-clay loam soils consisting of loessic sediments. The soil of the experimental plot was classified as fine-mixed thermic silty-loam Typic Argiudoll Rafaela Series (INTA, 1991). It has an A horizon (0.00-0.22 m), followed by a transitional AB horizon with high clay contents (0.22-0.32 m) and an argillic horizon Bt1 (0.33-0.70 m) underneath (INTA, 1991). The field was used for conventional tillage cultivation for more than 50 years, converted to no-tillage eight years before the experiment, and is currently used for a wheat-soybean sequence.

Soil sampling

During spring 2014, a field area of 3,200 m² (40 × 80 m) was tilled with an 11-shank chisel plow. The fixed shanks were spaced 0.35 m apart operating 0.18-0.20 m deep at a speed of 7 km h^-1. Before and after tillage, CI measurements were performed with a soil compaction meter (PNT-2000^®, DLG Automação) at sampling points of a 5 × 5 m horizon-aligned grid and in the top 0.40 m soil layer in 0.02 m intervals. The spatial structure at shorter distances was assessed by adding 32 extra points spaced 1 × 1 m to the original sampling scheme, drawing crosses over four randomly selected points (Figure 1). In total, 151 vertical CI profiles were recorded with a cone (diameter 1.9 cm, apex angle 30°) (Asabe Standards, 2006) at each sampling time (before and after chiseling). To characterize the mean soil water content (SWC) of the study area, soil cores were extracted from a 0.00-0.40 m layer on a 10 × 10 m grid and SWC was estimated by the gravimetric method for every 0.10 m interval.

Figure 1
Sampling scheme of cone index (CI). Markers indicate sampling points before (filled circles) and after tillage (empty circles).

Data analysis

The raw CI data (302 profiles) were pre-processed to eliminate systematic errors of soil penetrometer measurements. The distribution of CI values was summarized per layer using descriptive statistics and boxplots. Forty CI profiles were excluded prior to data analysis because they contained observations that exceeded the three-fold interquartile range from the first and third quartiles or were regarded as technical artifacts of the soil penetrometer measurements, such as reading failures. Data normality was assessed by the Shapiro-Wilks normality test per layer, and logarithmic transformation was performed if needed. The spatial autocorrelation of CI readings was examined by computing sample variograms in horizontal and vertical directions. Because of the non-stationarity of CI along the vertical direction, a polynomial model was developed using soil depth as explanatory variable. The trend coefficients were estimated by the generalized least squares (GLS) method and the sample variogram was computed using GLS residuals (Webster and Oliver, 2007).

Bootstrap procedure

The estimators of the mean CI values per depth and their sampling distributions for different sample sizes were obtained with a modified bootstrap procedure as follows:

1. One thousand bootstrap samples of size n were drawn with replacement from the available CI profiles for each sampling time. The sample sizes ranged from 2 to 130 or 140, depending on whether the sampling time was before or after tillage. Because of the spatial structure of CI along the vertical direction, each individual CI profile was regarded as resampling unit, so that the spatial autocorrelation was taken into account.

2. For each of the 1,000 bootstrap samples of a given size n, the vertical profiles of sample median and mean of CI values were obtained by calculating these statistics per layer.

3. Finally, the estimators of the vertical profile of mean CI values and their standard error (SE) were calculated by computing the arithmetic mean and the standard deviation of the bootstrapped sample median and mean of CI values of each layer.

Determination of the minimum sample size

The minimum sample size to estimate the vertical profile of mean CI values with different levels of precision and confidence using the sample mean and median as estimators was determined by a procedure adapted from Dane et al. (1986) and Melo-Filho et al. (2002). The vertical profile of population mean CI values was estimated using the full data and the intervals were calculated with an error margin of ± 10, 20, 30, and 40 %. Then, the confidence level was calculated for each sample size (n) as the proportion (P) of vertical bootstrap profiles of the sample median and mean of CI values within a given interval. Finally, the minimum sample size was determined by modeling the relation between P and n using a locally-weighted polynomial function (LOESS) and obtaining the inverse predictions by a linear approximation function. Confidence levels of 0.95, 0.90, and 0.80 were used.

In addition, the minimum sample size was approximated by using the conventional method based on the confidence interval of the mean (Equation 1). For each sampling time, the variance of the process was estimated using the information from the most variable layer and the minimum sample size was calculated for the same level of precision and confidence indicated above.

Data management and the bootstrap routine were carried out using the statistical programming language R (R Core Team, 2017). Geostatistical analyses were performed using the gstat package (Pebesma, 2004).

RESULTS AND DISCUSSION

Soil water contents at both sampling times were near field capacity and had low variation in the explored depth range; thus, no correction was applied to CI values (Table 1). The variability in CI values was high at both sampling times and decreased with depth (Figure 2). However, the sample CI profiles were more concentrated around the vertical profile of mean CI values before than after tillage (the average CV before and after tillage were 0.45 and 0.64, respectively). Before tillage, CVs >0.35 were observed from the surface to a depth of 0.20 m, whereas after tillage CV exceeded 0.35 in the entire depth range (Table 2). In general, the CVs reported in this study agree with those observed by Alesso et al. (2017) in a similar soil under no-tillage, but are higher than those found by Özgöz et al. (2007) and Cavalcante et al. (2011), especially in the surface layers.

Table 1
Mean ± standard deviation of soil water content in 0.10-m intervals to a depth of 0.40 m, before and after tillage

Soil layer	Before tillage	After tillage
	Soil water content
m	------- g g-1 -------
0.00-0.10	0.302 ± 0.017	0.271 ± 0.013
0.10-0.20	0.291 ± 0.012	0.279 ± 0.011
0.20-0.30	0.292 ± 0.015	0.280 ± 0.012
0.30-0.40	0.297 ± 0.015	0.290 ± 0.017

Figure 2
Spaghetti plots showing the variability of vertical profiles of cone index (CI) in MPa (gray lines) before and after tillage. Black lines represent the mean (solid line), median (dashed line), and the 2.5th and 97.5th quantiles (dotted lines).

Table 2
Summary statistics of cone index (CI, MPa) recorded before and after tillage in 0.02-m intervals from the soil surface to the maximum measured depth (0.40 m)

Depth	Before tillage (n = 130)					After tillage (n = 140)
	mean	CV	median	skw	p	mean	CV	median	skw	p
m
0.00	0.06	0.36	0.06	1.87	<0.0001	0.08	0.62	0.07	1.60	<0.0001
0.02	0.12	0.90	0.06	2.57	<0.0001	0.13	0.88	0.07	1.78	<0.0001
0.04	0.22	0.94	0.14	1.47	<0.0001	0.23	0.99	0.12	1.60	<0.0001
0.06	0.39	0.79	0.30	0.93	<0.0001	0.38	0.97	0.28	1.42	<0.0001
0.08	0.59	0.69	0.51	0.99	<0.0001	0.51	0.91	0.40	1.26	<0.0001
0.10	0.8	0.60	0.74	0.71	0.0013	0.64	0.86	0.53	1.02	<0.0001
0.12	0.99	0.52	0.92	0.56	0.0129	0.74	0.80	0.63	0.94	<0.0001
0.14	1.14	0.46	1.12	0.34	0.1834	0.84	0.76	0.70	0.86	<0.0001
0.16	1.27	0.41	1.32	0.05	0.4114	0.91	0.73	0.78	0.76	<0.0001
0.18	1.38	0.38	1.42	0.2	0.1784	0.98	0.70	0.81	0.67	<0.0001
0.20	1.46	0.37	1.49	0.21	0.1038	1.05	0.65	0.94	0.52	0.0003
0.22	1.51	0.34	1.51	0.24	0.2488	1.13	0.60	1.01	0.35	0.0053
0.24	1.53	0.32	1.55	0.09	0.6881	1.21	0.55	1.19	0.22	0.0301
0.26	1.53	0.30	1.58	-0.1	0.6348	1.26	0.52	1.27	0.22	0.1674
0.28	1.56	0.30	1.56	-0.14	0.5746	1.33	0.49	1.31	0.14	0.2225
0.30	1.58	0.30	1.62	-0.06	0.4969	1.38	0.45	1.46	-0.03	0.2374
0.32	1.62	0.29	1.62	-0.13	0.4389	1.43	0.42	1.51	-0.20	0.1148
0.34	1.67	0.29	1.66	-0.15	0.6051	1.47	0.39	1.50	-0.23	0.1241
0.36	1.77	0.29	1.72	-0.04	0.4064	1.52	0.37	1.57	-0.18	0.1480
0.38	1.86	0.29	1.82	0.10	0.5610	1.58	0.37	1.65	-0.07	0.4839
0.40	1.96	0.30	1.92	0.09	0.4273	1.65	0.35	1.71	-0.03	0.3466

• CV: coefficient of variation; skw: skewness; and p: p-value of the Shapiro-Wilks normality test.

Before tillage, data from the 0.00 to 0.12 m depth range had right-skewed distributions, whereas after tillage, skewness was observed even to a depth of 0.20 m. However, according to the Shapiro-Wilks test, the distribution of CI values departed from normality only in the upper 7 and 13 layers before and after tillage, respectively. These results showed that surface layers are more variable and that due to the skewness of CI data, the population mean of CI values in the surface layers would not be adequately estimated by the sample mean. Similar results were reported by Gubiani et al. (2011), who concluded that the sample mean represents CI values in compacted better than in chiseled soils, due to the effect of tillage on soil structure. In this case, data transformation should be applied to meet the assumptions, or a more robust estimator, such as the sample median, should be used instead of the sample mean. In this study, the logarithmic transformation did not approximate the CI data to a normal distribution. The sample mean and median did not differ strongly due to the large sample sizes (Table 2 and Figure 2). However, larger differences between the vertical profiles of sample mean and median CI values would be expected if the samples were smaller.

In the horizontal plane, the CI data showed no spatial structure at both sampling times, thus each profile could be regarded as an independent sample unit. The lack of spatial structure in the horizontal plane indicates that the scale of spatial continuity of this property was below the sampling scale due to the combination of small-scale variations and measurement errors. In contrast, in the vertical direction (1D), CI showed a strong spatial structure (Figure 3). Similar results were reported by Castrignanò et al. (2002) on a Typic Chromoxerert from southern Italy and by Veronesi et al. (2012) on a Haplic Cambisol in the Czech Republic. In this study, the fitted global trend models before and after tillage explained 40 and 57 % of the total variability of CI. In both cases, after trend removal, a spherical model was fitted to the sample variogram and the estimated range showed differences as a result of tillage. Before tillage, the CI measurements showed auto-correlation to a depth of 0.12 m whereas after tillage, this range of spatial dependence was extended to a depth of 0.16 m. The shorter range may be attributed to the layering produced by the continuous no-tillage system, inducing strong changes in the soil properties in deeper layers, whereas vertical tillage alters the soil structure by loosening the layers and extending the spatial continuity.

Figure 3
Sample and fitted isotropic variograms of the cone index (CI) data along vertical direction (1D) before and after tillage.

Sample size

The relation between sample size and the maximum relative error (RE) of the sample median and mean of CI values approximated by bootstrap before and after tillage is shown in figure 4. In general, the maximum RE of both estimators corresponds to the top 0.10 m of soil where the most variable CI readings were recorded. For both sampling times, the sample median of CI values showed higher RE compared to the sample mean over the entire range of sample size explored. In this study, the tillage increased the variability of CI readings resulting in higher RE of the sample mean or median of CI values after tillage for a given sample size. Similar results were reported by Özgöz et al. (2007) who concluded that all three tillage system evaluated (conventional, conservation, and reduced) increased variability of CI values. In addition, the RE associated with the sample median of CI values were greater than the RE of the sample mean because the latter is a more efficient estimator, i.e. less variance. As a result, the number of samples needed to achieve the same precision is also greater for the sample median. For example, a maximum RE of 20 % around the vertical profile of sample mean CI values could be expected with about 21 samples before tillage, whereas at least 24 observations are needed to achieve the same precision after tillage. In contrast, the same level of precision around the vertical profile of sample median CI values could be achieved with about 66 observations before tillage, whereas more than 139 are needed after tillage.

Figure 4
Relation between sample size (n) and the maximum relative error (RE) of the vertical profile of mean (solid line) and median (dashed line) cone index (CI) values approximated by bootstrapping before and after tillage for of the entire 0.00-0.40 m depth range.

The same general pattern of the relationship between sample size and error of the estimator was reported by Molin et al. (2012) and Tavares Filho and Ribon (2008) for Oxisols. However, in those studies the sample size above which no information is gained by adding extra observations was significantly smaller than the results reported in figure 4. This could be explained by the differences of variability between soils and by the fact that in those studies the CI data were collected or averaged per layer. The former study reveals the soil-specific nature of the sample size recommendations, thus the greater the soil variability, the higher are the sampling requirements. The latter article describes the smoothing effect of sample support or data processing on the variability (Gubiani et al., 2011), resulting in a smaller RE for the same sample size.

Although the approach described above is straightforward, it has a major drawback by using only the information contained in the most variable soil layer and is based on the assumption of normality of the population being sampled. The alternative method proposed in this study is based on a resampling method and integrates the information of the whole depth range down to 0.40 m, taking the vertical autocorrelation into account. The proportion of bootstrap vertical profiles of sample median and mean CI values falling within the intervals around the vertical profile of the population of mean CI values increases with the sample size for all error margin levels, as expected for both estimators (Figure 5). However, for the sample median, none of the curves reached the line P = 1, neither before nor after tillage. In contrast, for the sample mean, the curves ± 20, 30, and 40 % reached the plateau level within the available maximum sample size (n = 130 and 140) although the sampling sizes were always greater after tillage (Figure 6). These results show that the sampling distribution of the sample median was more variable than that of the sample mean. Also, they show the effect of soil tillage on soil variability because the relationship between sample size and proportion of bootstrap profiles within confidence intervals tended to be less steep after soil tillage.

Figure 5
Proportion of bootstrap vertical profiles of sample median of cone index (CI) values that fall within the ± 10, 20, 30, and 40 % of error around the vertical profile of mean CI values, before and after tillage.

Figure 6
Proportion of bootstrap vertical profiles of sample mean of cone index (CI) values that fall within the ± 10, 20, 30, and 40 % of error around the vertical profile of mean CI values, before and after tillage.

In general, the number of sample profiles required for the estimation of the vertical profile of mean CI values increased with the variability of CI readings and the desired level of precision and confidence (Table 3). The higher variability of CI observed after tillage resulted in greater sampling requirements for all levels of precision and confidence, independent of the estimator being used. In addition, at the same level of variability of CI readings, precision and confidence, the sampling requirements are higher when the profile of mean CI values is estimated by the sample medians. For example, to estimate the profile of mean CI values with an error of less than 20 % and a confidence level of 90 %, it would be necessary to take more than 129 and 139 sample profiles before and after tillage, respectively. In contrast, if the estimators were the sample means, the sampling requirements would drop to 74 and 97 sample profiles of CI before and after tillage, respectively. Estimating sampling needs with the conventional method, which assumes normal distribution of the CI data and is based solely on the information of the most variable layer, resulted in smaller sampling sizes. For instance, for the same level of precision and confidence, 64 and 81 sample profiles would be required before and after tillage, respectively, according to this method. This discrepancy between methods suggests that bootstrapping is a more robust method to assess true population variation based on information of the entire profile.

Table 3
Minimum sample sizes needed to estimate the vertical profile of mean cone index (CI) values with different levels of precision and confidence before and after soil tillage determined by conventional and bootstrap methods, using the vertical profile of sample mean and median values as estimators

Sampling moment	Method	Estimator	Relative error margin (%)
			± 0.10	± 0.20	± 0.30	± 0.40
			Confidence level = 95 %
Before tillage	Conventional	mean	361	100	49	25
	Bootstrap	mean	>129	98	43	31
		median	>129	>129	>129	>129
After tillage	Conventional	mean	400	100	49	25
	Bootstrap	mean	>139	124	53	36
		median	>139	>139	>139	>139
			Confidence level = 90 %
Before tillage	Conventional	mean	256	64	36	16
	Bootstrap	mean	>129	74	35	26
		median	>129	>129	>129	103
After tillage	Conventional	mean	289	81	36	25
	Boostrap	mean	>139	97	44	31
		median	>139	>139	>139	117
			Confidence level = 80 %
Before tillage	Conventional	mean	169	49	25	16
	Bootstrap	mean	>129	50	27	18
		median	>129	>129	84	57
After tillage	Conventional	mean	169	49	25	16
	Bootstrap	mean	>139	70	34	24
		median	>139	>139	98	74

In all cases, the estimated sample sizes (Table 3) exceeded the sampling recommendations reported in other studies (Tavares Filho and Ribon, 2008; Molin et al., 2012; Storck et al., 2016). For example, the general recommendation of 20 samples would result in an estimation of the vertical profile of mean CI values with an error near ± 40 %, the 80 % of the times at best, independently of the estimator or the sampling time. Nevertheless, the precision achieved with 15-20 samples is below than the 10 % threshold suggested by the above authors.

The bootstrap method can be used without requiring knowledge of the distribution of the sampling population, whereas the conventional sample size determination method depends on knowledge or assumptions about the distribution (Dane et al., 1986). Moreover, it is readily understood and easy to use, provided the assumptions are met, but depends strongly on how well the variance of the process is approximated (Han et al., 2016). On the other hand, bootstrapping methods are conceptually more complex and require computer software to perform the simulations, but are superior to the parametric methods, for capturing the variability more precisely and estimating the sampling distribution without assumptions. These features allow researchers to deal with distributions other than normal, as is common for soil properties. In particular, the method outlined here considers the vertical autocorrelation by performing the resampling procedure at the soil profile level.

CONCLUSIONS

The proposed bootstrap approach allows the approximation of the sampling distribution of the vertical profiles of soil cone index and the determination of the sample size required for the estimation of a vertical profile of mean CI values using different estimators and levels of precision and confidence. Unlike the conventional method, the proposed approach did not assume any theoretical distribution and uses the information of the whole soil profile including the vertical autocorrelation of CI readings, which is a great advantage.

Results of this study demonstrate that the bootstrap approach may be used to determine the implications of soil variability on the sampling efforts required for an accurate estimation of soil resistance in soils subjected to different soil management practices. However, more research is needed to explore the effect of different soil types, tillage practices, and penetrometers on the sample needs.

ACKNOWLEDGEMENTS

The authors are grateful to the Facultad de Ciencias Agrarias, Universidad Nacional del Litoral through (Esperanza, Argentina), and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Santa Fe, Argentina) for financial and operational support of this research by the grant PIP No. 677/Res. D5202. The assistance of Mr. Masola and undergraduate students at UNL-FCA is gratefully acknowledged.

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Publication Dates

• Publication in this collection
  2017

History

• Received
  20 Oct 2016
• Accepted
  12 July 2017