Prediction and spatial variability of soil dynamic properties in sugar cane fields of Sao Paulo State - Brazil R.B. Silva, K.P. Lanças and E.E.V. Miranda Fazenda Experimental Lageado, Departamento de Engenharia Rural - FCA/Unesp, Campus de Botucatu, São Paulo, BR, Caixa Postal 237
[email protected] Abstract The objective of this work was to model and diagnose the spatial variability of soil load support capacity (SLSC) in sugar cane crop fields, as well as to evaluate the management impact on Sao Paulo State soil structure. The investigated variables were: pressure preconsolidation (σp), apparent cohesion () and internal friction angle (). The conclusions from the results were that the models and spatial dependence maps constitute important tools in the prediction and location of the mechanical internal strength of soils cultivated with sugar cane. They will help future soil management decisions so that soil structure sustainability will not be compromised. Keywords: Soil compaction, preconsolidation pressure, shear stress, sugar cane, trafficability Introduction The continuous use of agricultural machines and implements to create appropriate soil conditions for sugar cane plant development has been causing irreversible damage to the structure of Brazilian agricultural soils. An alternative to this problem is the soil load support capacity (SLSC) models proposition, which estimates how much load the soil can stand without compromising its mechanical internal strength. In this case, the evaluation of pressure consolidation (σp), the maximum pressure that a partially saturated soil will support without suffering additional compaction (Dias Júnior & Pierce, 1995), as well as soil shear stress (τ) becomes an important factor. These are relevant soil/tire and soil/tool dynamic properties and their measurement and field localization can help to minimize the negative effects of traffic in sugar cane fields. Brazilian researchers have mainly used soil bulk density and cone index to evaluate soil compaction but they are insufficient. Therefore, Dias Júnior & Pierce (1996) proposed an exponential model (equation 1) that predicts σp as a function of soil moisture: σP = 10(a+bW)
(1)
Where: σp = preconsolidation pressure, kPa a,b = adjustment parameters W = soil moisture, kgkg-1 Silva et al. (2003c) verified inverse relationships between sugar cane productivity and values of σp Other authors have used this model in several conditions to quantify the maximum soil pressure (contact pressure) to be applied without causing additional compaction (Silva et al., 2001, Silva et al., 2003a, Silva et al., 2003b). Shear stress (τ) is a dynamic property that can also contribute to soil behavior prediction and, according to Terzaghi et al. (1996), the classical mechanics theories assume that instantaneous soil rupture occurs when τ is exceeded. The Coulomb equation (equation 2) is used to predict this value.
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According to Cooper & Nichols (1959) and Terzaghi et al. (1996), these equation parameters (c and φ) vary with soil particle size, moisture, soil tillage and other factors. τ = c + σntg φ
(2)
Where: τ = shear stress c = soil apparent cohesion, kPa σn = normal stress, kPa φ = soil internal friction angle, degree Munkholm et al. (2001) verified significant increases in the apparent cohesion when minimum cultivation compared to conventional cultivation was practised. Similar results were also found by Ball & O`Sullivan (1982). During harvest, the effects of combine compaction on shear stress in vertisols were evaluated by Radford et al. (2000). Servadio et al. (2001) verified that traffic intensity significantly changed the shear stress obtained by a vane shear test in the depth from 0.0 to 0.30 m. The authors also found high correlation between the shear stress and soil bulk density in the layers from 0.0 to 0.10 and from 0.10 to 0.20 m. Brazilian researchers are trying to establish relationships among τ and different types of soils, managements and tillage systems. Silva et al. (2004) studied the tillage system influence (no tilled and conventional) in Eutrorthox. Soil water content influenced the soil shear stress and the conventional system presented highest in the 0 to 0.05 m layer. Lately, spatial variability of agricultural soils has become important in this type of study. Precision agriculture has been used to localize soil sampling and to generate soil property maps, becoming one of the resources for evaluating and interpreting the mechanical and dynamic properties of agricultural soils. In this context, knowing and evaluating the impact of sugar cane cultivation on soil structure using SLSC models (equation 1) and shear stress (equation 2), as well as considering spatial variability represent important economic and environmental information. This might make possible preventative decisions where traffic and soil tillage are intensive activities. This work had as objective to model and diagnose the spatial variability of preconsolidation pressure and shear stress of areas cultivated with sugar cane, as well as evaluating the management impact on Sao Paulo State soil structure. Materials and methods The experimental field (8.34 ha) was located at 22° 38’ 09’’ S and 47° 41’ 03’’ WG, in the Piracicaba rural area (Sao Paulo State, Brazil) and has being cultivated with sugar cane, in the second harvest cycle. The soil was classified by EMBRAPA (1999) as a Eutrorthox. Soil sampling was done in a grid of 60 x 60 m, using a DGPS Ag GPS 132, Trimble with OmniStar correction. Compressibility models: σp measurements Undisturbed samples were collected and geocoded from two depths: 0 to 0.10 m (superficial layer - SL) and in the layer of greatest mechanical resistance (LGMR). The LGMR was previously identified by cone index (CI), obtained with an electronic-hydraulic penetrometer developed by Lanças & Santos (1998). The sampling rings had dimensions of 0.025 x 0.06 m height and diameter, respectively, and they were extracted from the soil (Figure 1) with the aid of a specifically
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developed sampler . Immediately after getting the samples, they were wrapped with paper film and liquid paraffin, to maintain the soil moisture, the soil stress history and the soil structure. The models for each layer were generated as a function of 5 different soil moistures with 3 repetitions. The samples were prepared in the laboratory (Figure 2) and soil moisture was equilibrated to the three soil consistency states (tenacity, friability, and plasticity), obtained after Atterberg limits determination (Liquid Limit, Plastic Limit and Shrinkage Limit), as proposed by Lambe (1951). They were then submitted to uniaxial loads (pressure levels: 25, 50, 100, 200, 400, 800 and 1600 kPa) using a consolidometer device, following the methodology described by Dias Júnior & Pierce (1996), until 90% of the maximum deformation in the sample (Taylor, 1971). These tests enabled the soil compression curve to be obtained. This curve describes the relationship between the logarithm of the applied pressure and bulk density or void ratio (Casagrande, 1936). The σp which divides the soil compression curve into a region of small elastic and recoverable deformation (secondary compression curve) and a region of plastic and recoverable deformation (virgin compression curve), was determined according to Dias Junior & Pierce (1995). equation 1 permitted estimation of σp as a function of different soil water contents (W). Shear stress models: c and φ measurements The soil apparent cohesion (c) and friction internal angle (φ) were obtained by adjusting the Coulomb equation (equation 2), usingand σn recorded in a Sheargraph device (Cohron, 1963). All
Figure 1. Undisturbed soil sample procedures.
ACORDING TO (SILVA, 2003)
DEFINING SOIL MOUISTURE (W)
IN FUNCTION CONSISTENCE STATES (TENACITY, FRIABILITY, PLASTICITY)
SATURATION SOIL SAMPLES
UNIAXIAL TEST
Figure 2. Laboratory procedures suggested by Silva et al. (2003b) for uniaxial tests.
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tests were done on the 0 - 0.10 m soil layer in plant row (PR) and traffic track (TT). To adjust the Coulomb equation, five normal stresses were applied by the Sheargraph: 21, 42, 63, 84 and 105 (kPa), according to Silva et al. (2003c). Statistical analysis The statistical differences between the equations estimated for the compressibility models (equation 1) and the Coulomb equation (equation 2) were verified using the F distribution (Snedecor & Cochran, 1989). The c and φ statistical differences between PR and TT were verified using ANOVA and the Scott & Knott (1974) mean test. The spatial dependence was evaluated by geostatistical analyses with the software GS+ for Windows 5.0, Beta version. Theoretical and experimental semivariograms as well as kriging interpolation were used to generate the SLSC, c and φ maps using software Surfer version 8.0. Results Compressibility models and spatial variability There were no statistical differences between the equations for SL and LGMR, according to Snedecor & Cochran (1989). Therefore, σp and (W) data were grouped allowing for the adjustment of only one equation for both layers (Figure 3). The determination coefficient (0.82) showed reasonable spatial variability from σp as a function of soil moisture. This variability is confirmed by the high residuals of the data in relation to the mean. Shear stress models and spatial variability The c and φ parameters and the Coulomb equations (equation 2) were statistically different (Snedecor & Cochran, 1989) at the 0.05 significance level, for plant row (PR) and traffic track (TT). The shear stress must be estimated with specific equations for each treatment (PR and TT). Figure 5 shows the Coulomb equations and the results of mean tests for both treatments, PR and
Observations Predicted (95%) Confidence (95%)
700 600
80 60 40
Residuals, %
500
Vp, kPa
100
400 300
20
Mean
0 -20 -40
200
-60
100
Vp= 10
(2.78-1.13W)
2
R =0,82*
-80 -100
0 0.0
0.1
0.2
0.3
Soil mouisture, kgkg-1
0.4
0.0
0.1
0.2
0.3
0.4
Soil mouisture, kgkg-1
Figure 3. Compressibility model for both layers (left) and the residuals of the data in relation to the mean (right).
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TT. The mean test (P > 0.0001) of Scott & Knott (1974), for c and φ, showed highest values in the traffic track (TT), which were statistically different from the plant row values. Figure 4 shows the spatial variability of σp for each layer (SL and LGRM) as a function of the three soil consistency states (tenacity, friability, and plasticity). There was a reduction in σp values with increase of soil moisture. Parameters of adjusted semivariograms are shown in Table 1. A spherical model gave the best fit for both variables (c and φ) and treatments (PR and TT) with determination coefficient (R2) varying from 0.82 to 0. 96. The range values were lower than the φ values, giving evidence of the independence of these parameters for the same type of soil.
m 0 25 50 75 100
ty aci Ten
ty aci Ten
y ilit
lity ab i F ri
ity stic Pla
it y s t ic P la
ab Fri
Y
Di re
Vp, kPa
cti o
X
io ect D ir
n, m
Y
n, m
Di
rec tio n
800 700 600 500 400 300 200 100 0
ire XD
n, m ctio
,m
Figure 4. σp (kPa) maps for the two layers: SL (left) and LGMR (right).
Mean values c (kPa) and I (degree) for PR and TT treatments
180 160
W, kPa
140
Treatments
120 100
PR TT
80 60
PR: W =17.315 + 1.1327Vn (R2 = 0.99**) TT: W = 22.868 + 1.2861V (R2 = 0.99**)
40
n
20 20
40
60
80
Vn, kPa
100
120
Parameters c I 17.50b 48.50b 23.00a 52.00a
Small letters compare the different treatment means at 5% Scott & Knott (1974) probability test.
Figure 5. Coulomb equations (left) and and φ mean test (right) for plant row (PR) and traffic track (TT).
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Table 1. Adjusted semivariogram parameters for c and φ in plant row (PR) and traffic track (TT). Treatments
Parameters
Model
Nugget
Sill
Range (m) R2
SDG1
PR
Cohesion, Friction angle, φ Cohesion, Friction angle, φ
Spherical Spherical Spherical Spherical
9 4 5.3 3.1
79 9 95.4 10
64 145 66 105
0.11 0.44 0.05 0.31
TT 1SDG
0.82 0.96 0.93 0.88
= Spatial Dependence Degree [(nugget/(nugget + sill)]
The values in the traffic track (TT) were higher than in the plant row (PR), as presented in Figure 6. The maps show that the c values were concentrated in the 15 to 30 kPa range for both treatments (PR and TT); whereas, in isolated parts, the observed values were in the 30 to 50 kPa range. It can be also clearly observed that the highest φ values were in the traffic track, mainly in the range 50 - 60º. The map for PR treatment shows the area divided into two significant parts (45 to 50º and 50 to 60º). m 0 25 50 75 100
Plant Row 7522250
7522220
7522220
7522190
7522190
7522160
7522160
7522130 c, kPa
7522100 7522250
Traffic Track
50
7522220
40
7522190
30
7522160 7522130 7522100 258450
258550
X Direction, m
258650
Plant Row
7522130 7522100
7522250
I kPa Traffic Track 60
7522220 7522190
55
20
7522160
50
10
7522130
0
258350
Y Direction, m
Y Direction, m
7522250
45
7522100 40
258350
258450
258550
258650
X Direcion, m
Figure 6. Variable c (left) and φ (right) maps for plant row (PR) and traffic track (TT)
Discussion The soil is deep, porous and well-drained and rather susceptible to compaction when trafficked with inadequate soil moisture i.e. with W above plasticity limits (plastic region). The values of σp (Figure 3) denote high consolidation below the tillage layer. This severe soil structure alteration is due to the high level of contact pressure imposed on the soil by the field traffic that exceeds the limit of soil internal mechanical resistance. The SLSC models constitute an important means to predict this limit as a function of soil moisture and, consequently, to manage the load limit that may be applied in the future. The maps shown in Figure 4 can be used to appropriately select the traffic in sugar cane areas and assist farmers to correctly prepare machinery (inflation pressure, tire type, contact area, and load wheel).
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The highest values of apparent cohesion (22.87 kPa) and φ (1.286) were observed in the traffic track (Figure 5), independently from the normal stress applied to the soil. This implies an increase of soil shear stress and, consequently, of compaction. The influence of soil porosity and particle size on c and φ were observed by Kézdi (1979) and Ayers (1987). Stafford & Tanner (1977) also reported that φ tends to increase when the soil porosity is reduced. Silva et al. (2004) found a direct correlation between traffic intensity and τ when comparing different managements. Koolen & Kuipers (1983) reported that these parameters are strongly affected by the normal stress. Positive correlation between soil bulk density and , in the traffic track, of two different tractors, was verified by Servadio et al. (2001). The geostatistical analysis (Table 1) showed that all estimated semivariogram ranges were higher than the soil sample grid (60 m). The ranges for c were 66 m in TT and 64 m in PR and for φ were 105 m for TT and 145 m PR. The values obtained for φ were variable with moderate spatial dependence (0.44 and 0.31); whereas, for , high spatial dependence (0.11 and 0.05) was observed, indicating high sensitivity of the geostatistical resources to the second parameter (c). The φ values are above the values reported in the literature (Terzaghi et al., 1996), but these results are related to the physical properties of the Eutrorthox (heavy clay soils). Silva et al. (2004) found values between 40 to 44 degrees for different soil managements. The highest φ values obtained in this study (50 - 60 degrees) confirm the severe conditions of management and degradation of soil cropped with sugar cane. Conclusions The SLSC was predicted satisfactorily from σp as a function of soil moisture. Apparent cohesion (c), internal friction angle (φ) and the Coulomb equation were significantly altered by traffic intensity. The σp, c and φ maps were shown to be important tools to localize and visualize soil compaction and mechanical resistance zones. They constitute a resource to evaluate traffic impact in areas cultivated with sugar cane. Acknowledgements Sao Paulo State Research Aid Foundation (FAPESP) for the Post-doctoral fellowship to the first author and to the Rural Engineering Department of Sao Paulo State University (FCA/UNESP), Botucatu Campus. References Ayers, P. D. 1987. Moisture and density effects on soil shear strength parameters for coarse grained soils. Transactions of the American Society of Agricultural Engineering 30 1282-1287. Ball. B. C.; O`Sullivan, M. F. 1982. Soil strength and crop emergence in direct drilled and ploughed cereal seedbeds in seven field experiments. Journal of Soil Science 33 609-622. Casagrande, A. 1936. The determination of the pre-consolidation load and its practical significance. In: Proceedings of the ICSMFE Conference on soil mechanics & foundations. Cambridge, UK. 3, 60-64 Cohron, G. T. 1963. Soil Sheargraph. Agricultural engineering, 44 554-556. Cooper, A. W.; E Nichols, M. L. 1959. Some observations on soil compaction tests. Agricultural Engineering 40 264-267. Dias Júnior, M. S.; Pierce, F. J. 1996.O processo de compactação do solo e sua modelagem (The process of compaction of the soil and its modeling). Brazilian Journal of Soil Science 20 175-182. Dias Júnior, M.S.; Pierce, F.J. 1995. A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology 8 139-151.
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EMPRESA BRASILEIRA DE PESQUISA AGROPECUÁRIA - EMBRAPA. 1999. Centro Nacional de Pesquisas de Solo. Sistema brasileiro de classificação de solos (Soil classification brazilian system). Rio de Janeiro, Brazil 412 pp. Kézdi, A. 1979. Soil physics: selected topics, developments in geotechnical engineering. Elsevier, Amsterdam, The Netherlands 22-203. Koolen, A. J.; Kuipers, H. 1983. Agricultural soil Mechanics. Springer-Verlag, Berlin, Germany. pp. 30-207. Lambe, T. W. 1951. Soil testing for engineers. John Wiley and Sons, New York. Lanças, K. P.; Santos, C. A. 1998. Penetrômetro Hidráulico-eletrônico equipado com DGPS para avaliação da compactação do solo (Hydraulic-electronic Penetrometer with DGPS for evaluation of the soil compaction). In: Ingeniería Rural y Mecanización Agraria en el Ámbito Latinoamericano: Anais del Congreso Latinoamericano de Ingeniería Rural, edited by Sociedade Latinoamericano de Ingeniería Rural La Plata, Ag. 570-576. Munkholm, L. J.; Schjonning, P.; Rasmussen, K. J. 2001. Non-inversion tillage effects on soil mechanical properties of a humid sandy loam. Soil and Tillage Research 62 1-14. Radford, B. J.; Bridge, B. J.; Davis, R. J.; Mcgarry, D.; Pillai, U. P.; Rickman, J. F.; Walsh, P. A.; Yule, D. F. 2000. Changes in the properties of a vertisol and responses of wheat after compaction with harvest traffic. Soil and Tillage Research 54 155-170. Scott, A. J.; Knott, M. 1974. A cluster analysis methods for grouping means in the analysis of variance. Biometrics 30 507-512. Servadio, P.; Marsili, A. Paglia, M.; Pellegrine, S.; Vignozzi, N. 2001. Effects on some clay qualities following the passage of rubber-tracked and wheeled tractors in central Italy. Soil and Tillage Research 61 143-155. Silva, R. B.; Dias Junior, M. S.; Santos, F. L.; Franz, C.A.B. 2004. Resistência ao cisalhamento de um latossolo sob diferentes uso e manejo (Shear strength of a Eutrorthox under different use and management). Brazilian Journal of Soil Science 28 165-173. Silva, R. B.; Dias Junior, M. S.; Santos, F. L; Franz, C. A. B. 2003a. Influência do preparo inicial sobre a estrutura do solo quando da adoção do sistema plantio direto, avaliada por meio da pressão de preconsolidação (Influence of initial tillage operations on the soil structure appraised through the preconsolidation pressure when adopting the no till system). Brazilian Journal of Soil Science 23 219-226. Silva, R. B.; Lima, J. M.; Dias Junior, M. S. 2001. Alterações de propriedades físicas e hídricas de um Latossolo Vermelho distrófico pela adsorção de fósforo (Physical and hydraulic properties changed by phosphorus adsorption in Eutrorthox). Brazilian Journal of Soil Science 25 791-798. Silva, R. B; Miranda; E.E.V; Lanças, K. P.; Costa, E.M. 2003c. Influência da Resistência mecânica do solo na produtividade de cana-de-açúcar, avaliada através da pressão de preconsolidação (Effect of mechanical resistance of soil in sugar cane productivity, evaluated by pressure preconsolidation). In: Solo: Alicerce dos sistemas de produção: Anais do XXIX Congresso Brasileiro de Ciência do Solo, edited by SBCS, Ribeirão Preto, SP. (Divulgação digital). 4pp. Silva, R.B.; Dias Junior, M.S.; Silva, F. A. M; Fole, S. M. 2003b. O tráfego de máquinas agrícolas e as propriedades físicas, hídricas e mecânicas de um latossolo dos cerrados (Traffic of tillage tools on physical, hydraulic and mechanical properties of a Eutrorthox). Brazilian Journal of Soil Science 27 973983. Snedecor, G. W.; Cochran, W. G. 1989. Statistical methods. 8th. ed. Iowa State University, Ames, USA. 503pp. Stafford, J. V.; Tanner, D. W. 1977. The frictional characteristics of steel sliding on soil. Journal of Soil Science 28 (4) 541-553 Taylor, H.M. 1971. Effects of soil strength on seedling emergence, root growth and crop yield. In: Barnes, K. K; Carleton, W. M.; Taylor, H. M; Throckmorton, R. I.; Vandenberg, G. E. Compaction of agricultural soils. ASAE, St. Joseph, MI, USA, 292-305. (Monograph) Terzaghi, K.; Percl, R. B.; Mesri, G. 1996. Soil mechanics in engineering practice. 3rd. ed. John Wiley and Sons, New York. 549 pp.
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