Sampling plan for using a motorized penetrometer in soil compaction evaluation

Storck, Lindolfo; Kobata, Sarha Giovana Kazue; Brum, Betânia; Soares, André Brugnara; Modolo, Alcir José; Assmann, Tangriani Simioni

doi:10.1590/1807-1929/agriambi.v20n3p250-255

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…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.…”

Section: Sample Sizementioning

confidence: 62%

Estimating Sample Size of Soil Cone Index Profiles by Bootstrapping

Alesso

Masola

Carrizo

et al. 2017

Rev. Bras. Ciênc. Solo

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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 m 2 (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.

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Section: Sample Sizementioning

confidence: 62%

Estimating Sample Size of Soil Cone Index Profiles by Bootstrapping

Alesso

Masola

Carrizo

et al. 2017

Rev. Bras. Ciênc. Solo

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“…This result is due to higher variation in the SPR values in this soil layer, as was previously discussed for the item numbers of subsamples per sampling point. Tavares Filho and Ribon (2008) compared no-tillage and conventional tillage systems, and Storck et al (2016) studied an integrated crop-livestock system; both studies also found that the 0.00-0.10 m layer presented the highest variations and consequently needed a larger number of subsamples than any other soil layer to achieve a good level of reliability. However, considering that the surface layer of the soil under a no-tillage system is periodically disturbed during crop sowing, which minimizes possible physical restrictions to plant root growth, the decision of the subsample numbers per sampling point to provide penetration resistance measurements should be based on the microvariability of this parameter in the deeper soils layers (Moreira et al, 2016).…”

Section: Quality Of the Thematic Spr Maps In The Function Of The Numbmentioning

confidence: 99%

“…The SPR is usually quantified using traditional sampling methods, which include the collection of various subsamples (e.g., 12-15) in a random manner along the field and are considered independent samples (Tavares-Filho & Ribon, 2008;Storck et al, 2016). As a result, a mean SPR value is obtained for and applied to the total sampled area.…”

Section: Introductionmentioning

confidence: 99%

“…Measurements of SPR values are highly influenced by diverse intrinsic (e.g., soil moisture, texture and structure) and extrinsic (e.g., management system) soil factors. As consequence, high coefficients of variation are usually observed (Beutler et al, 2007;Storck et al, 2016). Therefore, to correctly determine the spatial variability of the SPR, it is crucial to establish an adequate density of sampling points per area and select the number of subsamples that best represents each sampling point.…”

Section: Introductionmentioning

confidence: 99%

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<b>Soil penetration resistance mapping quality: effect of the number of subsamples

et al. 2018

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There is no consensus in the literature regarding how many subsamples are needed to perform accurate on-farm soil penetration resistance (SPR) mapping. Therefore, the objective of this study was to define the number of subsamples per sampling point needed to quantify the SPR. The experiment was performed in a 4.7 ha area and employed a 50 × 50 m grid system (18 sampling points). The SPR was evaluated using a digital penetrometer in two different years with 1, 2, 3, 4, 5, 6, 9, 12, and 15 subsamples per sampling point. The SPR maps produced with increasing numbers of subsamples were compared to the reference maps (15 subsamples) using the relative deviation coefficient and Pearson´s linear correlation. A reduction in the number of subsamples promoted an increase in the variability of the SPR data. Generally, the results from this study suggest the use of at least four subsamples per sampling point to achieve SPR maps with a coefficient of relative deviation less than 10% (30% maximum error per point around the mean) and significant correlation with the reference maps (15 subsamples).

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Preliminary assessment of river ecosystem services in the volcanic area of Mount Merapi, Indonesia

Sunardi,

Dede,

Wibowo

et al. 2024

Aquat Ecol

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River ecosystem services (RES) are vulnerable to landscape changes mainly by volcanic eruptions. Therefore, this study aims to assess RES in the volcanic area which was affected by the major and minor eruptions of Mount Merapi, Indonesia. The RES referred to the regulating and supporting services of the Krasak River in Jogjakarta. The research involved collecting water and biodiversity samples from two distinct Merapi’s hazard zones (KRB I and KRB II) along the river. Parameters related to regulating services, such as particulate, organic, and nutrient purification, biological control, as well as supporting services like primary productivity, were quantified. We conducted an analysis to understand how landscape conditions interacted with these parameters and employed the t-test to assess differences in RES between the two KRBs. Our findings revealed that the Krasak River exhibited a range of values, including 2.40–5.95 mg/l for Biological Oxygen Demand (BOD), 0.61–3.41 mg/l for nitrate, 0.02–0.11 mg/l for phosphate, 160–60,000 MPN/100 ml for coliform, and 156.3–937 µg/l for chlorophyll-A. These values demonstrated the river’s capacity to perform both regulation and support services. However, certain segments showed variations in ecosystem services, possibly due to the presence of autochthonous matter from aquatic organisms and decomposing organic matters. This showed that volcanic eruption and landscape are closely linked with the water quality and aquatic biodiversity, which affect the ecosystem services.

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Sampling plan for using a motorized penetrometer in soil compaction evaluation

Cited by 4 publications

References 18 publications

Estimating Sample Size of Soil Cone Index Profiles by Bootstrapping

Estimating Sample Size of Soil Cone Index Profiles by Bootstrapping

<b>Soil penetration resistance mapping quality: effect of the number of subsamples

Preliminary assessment of river ecosystem services in the volcanic area of Mount Merapi, Indonesia

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