Evaluating the effects of tillage techniques on soil hydro-physical properties in Guinea Savanna of Nigeria

Soil & Tillage Research 126 (2013) 159–168

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Evaluating the effects of tillage techniques on soil hydro-physical properties in Guinea Savanna of Nigeria S.T. Abu a,*, I.U. Abubakar b a b

Department of Soil Science, Faculty of Agriculture/Institute for Agricultural Research, P.M.B. 1044, Ahmadu Bello University, Samaru-Zaria, Nigeria Department of Agronomy, Faculty of Agriculture/Institute for Agricultural Research, P.M.B. 1044, Ahmadu Bello University, Samaru-Zaria, Nigeria

A R T I C L E I N F O

A B S T R A C T

Article history: Received 11 July 2012 Received in revised form 7 September 2012 Accepted 23 September 2012

This study aimed at evaluating the effect of four tillage techniques: no-till (NT), minimum tillage (MT), contour tillage (CoT) and conventional tillage (CT) on soil hydraulic properties and physical quality using RETC (retention curve) computer model. Particle size distribution, bulk density and water retention at various soil matric potentials were determined using standard methods and the data were fitted into RETC computer programme for prediction of water retention curve and their analysis. Findings are: (1) the highest amount of water retained at all soil matric potential ranges and soil depths in the NT plot. (2) Pore-size classes varied inconsistently with depth and matric potential ranges in the tillage treatments, but predominantly greater equivalent pore diameter was observed in CT plots at most of the potential ranges. (3) In soil physical quality index 7.9, 11.5 and 18.3% reduction at 0–5 cm depth and 0.1, 1.6 and 4.2% at 5–15 cm depth were observed in MT, CoT and CT treatments respectively compared to NT. (4) The RETC model described the measured and predicted related hydraulic properties of the tillage techniques used in soybean cultivation. ß 2012 Elsevier B.V. All rights reserved.

Keywords: RETC model Soil water retention Saturated hydraulic conductivity Soil physical quality index

1. Introduction In the northern Guinea Savanna of Nigeria, agricultural production is mainly constrained by low levels of soil organic matter, low nutrient status and water holding capacity. Due to the fragile nature of the soils, they degrade rapidly under continuous and intensive cultivation. Characterizing changes of soil hydraulic properties arising from land use is important for applications in hydrology, soil water management and environmental conservation. Hydraulic properties are the key parameters in any quantitative description of water flow into and through the unsaturated soil zones (van Genuchten et al., 1992). These properties are determined by the geometry of the pore space. Tillage operations can modify the geometry of the pore spaces which consequently lead to temporal variation in the fragile nature of soil surface macropores, their ventedness and connectedness as well as the hydraulic character of tilled soil (Carter, 1988; Ogden et al., 1999). These effects decline with time as the soil matrix reconsolidate. The size and continuity of macropores in the surface soil often control the rate of water entering into the root zone (Hamblin, 1984). Reports by several researchers on the effects of tillage methods on the soil hydraulic properties under well-

* Corresponding author. Tel.: +234 07034761406. E-mail address: [email protected] (S.T. Abu). 0167-1987/$ – see front matter ß 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.still.2012.09.003

structured soil conditions are not always consistent across locations, soils and experiment designs (Green et al., 2003). The estimation of soil hydraulic properties is a fundamental step for quantifying water and solute movement in the vadose zone (Ventrella et al., 2005). Preferential flow in structured media (movement of free water and any solutes through both macroporous soils and fractured rocks) can be described using a variety of dual-porosity, dual-permeability, multi-porosity, and/or multipermeability models (Sˇimu˚nek et al., 2003; Sˇimu˚nek and van Genuchten, 2008). While dual-porosity models assume that water in the matrix is stagnant (i.e., treat both the pore regions as homogenous media with separate hydraulic and solute transport properties), dual-permeability models allow for water flow in the matrix as well. A large number of models using the two-domain or multidomain concept have been used to describe water flow and/or solute transport in macroporous soils (e.g., Bruggeman and Mostaghimi, 1991; Hoogmoed and Bouma, 1980), unsaturated fractured rocks (Berkowitz et al., 1988; Dudley et al., 1988), and fissured groundwater systems (Duguid and Lee, 1977; Bibby, 1981). SIMULAT, a one-dimensional dual-porosity model (Diekkruger, 1996; Diekkruger and Richter, 1996) was developed to enable the calculation of transport and transformation of biodegradable substances as nitrogen, sulfur and pesticides in the unsaturated/ saturated zone of the soil. It consists of submodels for the calculation of macropore flow, infiltration, runoff, evapotranspiration, plant growth, interception and heat flux in the soil. Water and

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matter transport in the soil matrix is calculated by the Richards’ equation and the convection–dispersion equation, respectively. Modeling approaches in simulation of nonequilibrium water flow and solute transport in the unsaturated zone range from relatively simple analytical solutions for solute transport (e.g., van Genuchten, 1981; Toride et al., 1993) to complex numerical codes (e.g., Sˇimu˚nek and van Genuchten, 2008; Jacques and Sˇimu˚nek, 2005). Approaches to calculating water flow in macropores or interaggregate pores range from those invoking Poiseuille’s equation, the Green and Ampt or Philip infiltration models, the kinematic wave equation (Lighthill and Whitham, 1955), and the Richards’ (1931) equation (Gerke and van Genuchten, 1993a,b, 1996). Germann and Di Pietro (1999) used analytical kinematic wave theory to describe macropore flow. The theory is based on Newton’s law of shear which Germann (1990), among others, applied to flow in structured and unsaturated soils. Chen and Wagenet (1992) simulated water and chemical transport by combining the Richards (1931) equation for transport in the soil domain with the Hagen–Poiseuille and Chezy–Manning equations for macropore transport. Jarvis (1994) developed a physically based model (MACRO) of water and solute transport in macroporous soil. van Genuchten (1980) and Van Genuchten and Nielsen (1985) introduced an empirical relationship for describing the cumulative particle size distribution and water retention curve that could be coupled to the model of Mualem (1976) to provide predicted unsaturated hydraulic conductivity from soil water-retention data. These methods are, generally, based on statistical pore-size distribution models (Mualem, 1986) to obtain a predictive equation for the unsaturated hydraulic conductivity function in terms of soil water retention parameters. RETC (retention curve) is a computer program developed to carry out lengthy numeric computation required to estimate the model parameters. It enables analyses of soil water retention and hydraulic conductivity functions of unsaturated soils (van Genuchten et al., 1992). The water retention relationship can be described using any of a number of empirical equations that have proper shape. The program allows use of two of these, the Brooks and Corey (1964) equation:

uðhÞ ¼ ur þ

us  ur n ðahÞ

(1)

and the van Genuchten (1980):

uðhÞ ¼ ur þ

ðu s  u r Þ n m

½1 þ ðahÞ 

(2)

where h is the suction head form of the matric potential (L) having positive values, u is the volumetric water content (cm3 cm3), ur and us are the residual and saturated water contents, respectively; and a (inverse of the air entry potential), m and n (associated with the pore-size distribution) are fitting constants. The RETC program uses the approach of either Burdine (1953) or Mualem (1976). For either approach, the relative conductivity, Kr can be written as: K r ðSe Þ ¼ ðSe Þl

hðSe Þ hð1Þ

(3)

where Se is the effective saturation: Se = (u  ur)/(us  ur), l is fitting parameter often taken as 0.5, and the mathematical form of the arbitrary function h(Se) depends on which model is used. Using the Mualem (1976) model:

hðSe Þ ¼

"Z

Se 0

#2 1 dx hðxÞ

Combining Eqs. (2) and (4) and the condition m = 1  1/n allows the hydraulic conductivity, K(Se), to be expressed as: 2 1=m m

KðSe Þ ¼ K s SLe ½1  ðSe

A nonlinear least-squares optimization approach is used in this program to estimate the unknown model parameters from observed retention and/or conductivity or diffusivity data. The model parameters can be determined using only retention data or both retention and conductivity data. The use of this program also optimizes the required parameters for water flow models under unsaturated soil conditions. The initial parameter estimates that are near to the optimal are optimized through successive iteration until the parameters attain constant and optimal values. The process is referred to as ‘‘refining’’. The aim of curve fitting process is to find an equation that maximizes the sum of squares associated with the model, while it minimizes the residual sum of squares (van Genuchten et al., 1992). This study aimed at testing the applicability of RETC computer code for evaluating the effects of various tillage techniques (conventional and conservation tillage techniques) on soil hydraulic behavior and physical quality. We hypothesized that significant differences in soil hydraulic properties and soil physical quality were caused by tillage techniques. 2. Materials and methods 2.1. Site description Present research was conducted in the experimental field of the Institute for Agricultural Research (IAR), Ahmadu Bello University, Samaru-Zaria, Nigeria which was left fallow for 6 years (up to 2009) and later subjected to various tillage techniques continuously for 2 years (2010 and 2011). The site is located at Samaru at Latitude 11809.5010 N and Longitude 7838.1540 E at an altitude of 686 m above sea level. The soil in the site is characterized by Kaolinitic clay mineralogy, loamy textural class and classified as Kanhaplic Haplustalf (USDA) by Moberg and Esu (1991). The area is situated in the Northern Guinea Savanna ecology of Nigeria with monomodal annual rainfall of about 1011  161 mm concentrated almost entirely in the 5 months (May/June to September/October). The mean daily minimum and maximum temperature ranges between 15 8C and 38 8C, respectively. 2.2. Experimental design The experimental field was divided into four blocks and each block was subdivided into 4 plots of 20 m  10 m in size and separated by 1 m buffer zone. Each plot in a block represents a tillage technique namely: (1) no-tillage (NT), (2) minimum tillage (MT), (3) contour tillage (CoT) and (4) conventional tillage (CT) as described in Table 1. Each block represents a replication and the

Table 1 Description of tillage techniques. Treatment

Description

No-tillage (NT) Minimum tillage (MT) Contour tillage (CoT)

Direct drilling method, weed control One pass of disk harrow Moldboard plow + one passes of disk harrow + one pass of Ox-drawn ridger along the contour of the field Moldboard plow + one passes of disk harrow + one pass of tractor-drawn ridger (straight ridging)

Conventional tillage (CT)

(4)

(5)

Þ 

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tillage treatments were arranged in a randomized complete block design.

Table 2 Particle size distribution and bulk density at various soil depths as influenced by tillage techniques.

2.3. Soil sampling and analysis

Treatments

Undisturbed cylinder of 98.125 cm3 volume and augered soil samples (disturbed) were collected from each plot at the 0–5, 5–15, and 15–30 cm depths in 3 replicates after harvest (in October) in 2010 and 2011 seasons. The disturbed augered samples, after airdrying and passing through 2 mm sieve, were used for determination of particle size distribution by the Bouyoucos hydrometer (Gee and Or, 2002). Bulk density (g cm3) was determined using core method as described by Anderson and Ingram (1993). For estimation of the unsaturated hydraulic conductivity values, water retention on undisturbed cylinder samples was measured. Water retained (cm3 cm3) at various soil matric potentials (0, 10, 20, 33, 40, 60, 100, 150, 200, 300, 500, 700, 1000 and 1500 kPa) were determined using sand box instrument and pressure plate apparatus (Klute, 1986). The water contents, u, were measured gravimetrically after ovendrying. The saturated hydraulic conductivity (Ks) was determined by constant head method (Klute and Dirksen, 1986) and computed by applying the Darcy’s law. The RETC (retention curve) computer code developed by van Genuchten et al. (1991) for fitting soil water retention, u(h), and hydraulic conductivity vs. tension, K(Se) data to models of the u(h) and K(h) functions was used to fit our data to solve van Genuchten’s (1980) water retention model presented in Eqs. (1) and (2). ‘‘Rosetta program’’, was used to obtain the closed form expressions of van Genuchten parameters (us, ur, a and n) from the values of particle size distribution, soil bulk density and volumetric soil water contents at 33 and 1500 khPa (Schaap et al., 1998). Output data of ‘‘Rosetta program’’ were used in RETC as input data besides the determined values of soil water retention. Output file of RETC run, which include measured and fitted relationships among pF matric potential (hPa) and soil water content (cm3 cm3) was converted to ‘‘Excel’’ file for statistical analysis. Water content at the inflection point, uINFL was calculated from the parameters of the fitted van Genuchten equation using the equation of Dexter and Bird (2001):

0–5 cm NT MT CoT CT 5–15 cm NT MT CoT CT 15–30 cm NT MT CoT CT



uINFL ¼ ðus  ur Þ 1 þ

 1 m þ ur m

(6)

161

Sand (%)

Silt (%)

Clay (%)

Textural class

Bulk density (g cm3)

46.70a 46.00a 46.73a 47.23a

43.50a 42.80a 41.23a 40.40a

9.80b 11.20ab 12.03a 12.37a

Loam Loam Loam Loam

1.45a 1.44a 1.43a 1.46a

45.63a 46.67a 44.47a 44.97a

42.57a 40.43a 41.40a 40.20a

11.80c 12.90b 14.13a 14.83a

Loam Loam Loam Loam

1.47a 1.45a 1.45a 1.48a

42.63a 42.93a 45.20a 45.40a

43.50a 42.03ab 38.83b 38.67b

13.87b 15.03a 15.77a 16.13a

Loam Loam Loam Loam

1.49ab 1.47b 1.47b 1.50a

NT, no-till; MT, minimum tillage; CoT, contour tillage; CT, conventional tillage. Means with the same letter in a column are not significantly different at 5% level of probability using Duncan’s Multiple Range Test (DMRT).

The slope of the water retention curves at the inflection point, S considered as soil physical quality index, was calculated according to Dexter (2004):   1 ð1þmÞ S ¼ nðu s  u r Þ 1 þ m

(7)

where us and ur are the gravimetric water content at saturation and the residual water content, respectively, and m is a shape parameter. Soil pore size distribution data was calculated from the predicted soil water retention data using the theoretical relation between soil water characteristic and distribution of pore sizes (Vomocil, 1965). The calculation was done for matric potential ranges of 7.5E2 to 6.3 kPa defining range of distribution of pores draining quickly permeating gravitational water; 6.3 to 33 kPa, being range of matric potential where pores draining slowly permeating gravitational water as well as water in the large capillaries occur; 33 to 100 kPa, as range of distribution of pores draining capillary water easily accessible for plants exist; 100 to 1500 kPa considered as range of distribution of pores draining capillary water accessible for plants with difficulty and above 1500 kPa being range of distribution of pores draining water not

Table 3 Soil water content at various pF matric potentials as influenced by tillage techniques at various depths. pF (kPa)

Soil water content (cm3 cm3) 0–5 cm

0.00 1.00 1.30 1.52 1.60 1.78 2.00 2.18 2.30 2.48 2.70 2.85 3.00 3.18

5–15 cm

15–30 cm

NT

MT

CoT

CT

NT

MT

CoT

CT

NT

MT

CoT

CT

0.2043a 0.2035a 0.1985a 0.1909a 0.1900a 0.1776a 0.1633a 0.1523a 0.1374a 0.1214a 0.1068a 0.0900a 0.0783a 0.0542a

0.2015a 0.2007a 0.1968a 0.1885a 0.1871a 0.1763ba 0.1579b 0.1468ba 0.1319b 0.1175ba 0.0988b 0.0838b 0.0724ab 0.0483ab

0.1935b 0.1933b 0.1890b 0.1826b 0.1814b 0.1731b 0.1565b 0.1438bc 0.1268c 0.1158b 0.0945bc 0.0802bc 0.0674bc 0.0433bc

0.1910b 0.1908b 0.1875b 0.1781c 0.1765c 0.1688c 0.1519c 0.1398c 0.1226c 0.1143b 0.0895c 0.0762c 0.0635c 0.0386c

0.1998a 0.1996a 0.1973a 0.1883a 0.1874a 0.1745a 0.1612a 0.1472a 0.1382a 0.1199a 0.1034a 0.0883a 0.0755a 0.0467a

0.1967a 0.1965a 0.1907b 0.1827b 0.1826b 0.1709ba 0.1517b 0.1384b 0.1277b 0.1122b 0.0947b 0.0800b 0.0687b 0.0459a

0.1913b 0.1912b 0.1864c 0.1773c 0.1775c 0.1685b 0.1509b 0.1364b 0.1228b 0.1115b 0.0914b 0.0769b 0.0632c 0.0457a

0.1867c 0.1864c 0.1782d 0.1715d 0.1702d 0.1605c 0.1399c 0.1297b 0.1129c 0.0987c 0.0808c 0.0692c 0.0543d 0.0452a

0.1930a 0.1928a 0.1901a 0.1872a 0.1860a 0.1670a 0.1507a 0.1350a 0.1145a 0.0989a 0.0816a 0.0644a 0.0609a 0.0570a

0.1900ba 0.1888ba 0.1839b 0.1797b 0.1781b 0.1618ab 0.1404b 0.1229b 0.1086a 0.0935ab 0.0795ab 0.0612a 0.0562a 0.0520a

0.1846b 0.1844b 0.1801c 0.1751b 0.1736c 0.1599b 0.1386b 0.1254b 0.1078a 0.0903bc 0.0776b 0.0607a 0.0573a 0.0552a

0.1794c 0.1791c 0.1728d 0.1656c 0.1642d 0.1503c 0.1286c 0.1113c 0.1002b 0.0853c 0.0718c 0.0587a 0.0555a 0.0523a

NT, no-till; MT, minimum tillage; CoT, contour tillage; CT, conventional tillage. Means with the same letter in a column are not significantly different at 5% level of probability using Duncan’s Multiple Range Test (DMRT).

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Table 4 Fitted values of van Genuchten parameters by RETC computer program under various tillage treatments and soil depths. Treatments 0–5 cm NT MT CoT CT 5–15 cm NT MT CoT CT 15–30 cm NT MT CoT CT

us (cm3 cm3)

a (cm1)

n

SSQ before refining

SSQ after refining

Ks measured

Ks predicted

0.2037* (0.0026) 0.2014* (0.0025) 0.1931* (0.0024) 0.1898* (0.0028)

0.0098* (0.0012) 0.0100* (0.0011) 0.0085* (0.0009) 0.0009* (0.0001)

1.4280* (0.0282) 1.4558* (0.0279) 1.5045* (0.0329) 1.5167* (0.0386)

0.12859

0.00018

35.22a

33.66a

0.14312

0.00017

35.97a

31.61a

0.15617

0.00018

36.99a

32.14a

0.15254

0.00024

35.69a

30.37a

0.1997* (0.0032) 0.1970* (0.0025) 0.1915* (0.0020) 0.1867* (0.0014)

0.0089* (0.0014) 0.0108* (0.0013) 0.0099* (0.0009) 0.0118* (0.0008)

1.4621* (0.0393) 1.4515* (0.0274) 1.4760* (0.0240) 1.4755* (0.0164)

0.13179

0.00037

31.03a

28.38a

0.15096

0.00016

33.99a

30.33a

0.15166

0.00011

31.58a

28.92a

0.15445

0.00005

32.15a

28.37a

0.1938* (0.0021 0.1903* (0.0020) 0.1850* (0.0018) 0.1800* (0.0012)

0.0102* (0.0008) 0.0119* (0.0010) 0.0111* (0.0008) 0.0137* (0.0007)

1.8009* (0.1179) 1.7155* (0.1029) 1.7915* (0.1021) 1.7420* (0.0617)

0.16585

0.00010

26.47a

24.99b

0.17851

0.00008

27.62a

26.65ba

0.18447

0.00007

32.31a

29.48a

0.17815

0.00003

32.03a

29.30a

NT, no-till; MT, minimum tillage; CoT, contour tillage; CT, conventional tillage, us, soil water at saturation (cm3 cm3), a, water release parameter (cm1), n, water release parameter (dimensionless), SSQ, sum of squares (residual), Ks, saturated hydraulic conductivity. Figures in bracket are standard error of coefficient. Means with the same letter in a column are not significantly different at 5% level of probability using Duncan’s Multiple Range Test (DMRT).

useful for plants. Equivalent pore diameter (EPD) of a given matric potential was estimated according to the following expression that relates the suction applied to a water column as a function of the capillary radii (the capillary rise equation): 4s cos a EPD ¼ rgh

(8)

where s is the surface tension of water; cos a is the cosinus of the angle a displayed by the water meniscus; r is the water specific weight; g the gravity acceleration and h the matric potential. At 22 8C the value of s become 0.07357 kg s1 and a = 0, the capillary rise equation can be reduced to the following expression (Marshall and Holmes, 1988): EPD ¼

300 h

(9)

where the equivalent pore diameter (EPD) of the smallest pore (mm) drained at matric potential of h (kPa). Pore size distribution was presented as percent pore volume of the total porosity occurring within a given range of matric potential. The determined parameters of particle size fractions, soil bulk density, water content at various water potentials, physical quality index and van Genuchten parameters were subjected to single factor analysis of variance (ANOVA) using SAS v. 9 computer program (SAS Institute Inc, 1992). Significantly different means were separate using Duncan’s Multiple Range Test (DMRT) at 5% level of probability. The predicted pore size classes and unsaturated hydraulic conductivity values were subjected to two-sample paired t-test analysis under which all possible pairs of the tillage treatments were compared. Significantly different means were separate using standard error of mean (SEM) at 5% level of probability.

3. Results and discussion 3.1. Particle size distribution and soil bulk density Data on particle size distribution and bulk density (rb) are contained in Table 2. Analysis of results on particle size distribution depicted weak dependence of sand distribution on the tillage techniques at all the depths. Similarly, silt distribution at 0–5 and 5–15 cm soil depths were not significantly influenced by the tillage techniques, however soil subjected to NT had significantly (p < 0.05) higher silt content at 15–30 cm depth compared to the one under the other tillage treatments. The distribution of clay was strongly dependant on the tillage techniques at all the soil depths. The highest value was recorded with conventionally tilled (CT) soil. The soil exhibited loamy textural class under all the tillage techniques and soil depths. This suggests that texture is an inherent static property of soil and therefore is not influenced by the management practices. Such static soil physical property is usually influenced by geologic history and climatic conditions (Franzlubbers and Haney, 2006). The increased amount of clay size fraction from NT to MT to CoT and to CT could be due to tillage induced migration of the particle size class from the original portion to other portion within the soil and suggests that the intensity of tillage determine the extent of the movement. Considering the bulk density (rb), the soil subjected to the various tillage techniques depicted no significant differences in the mean values at 0–5 and 5–15 cm depths. However, soil under CT had significantly higher rb mean value than the other tillage techniques at 15–30 cm depths, though statistically at par in the value with the one under NT. While Horn (2004), Fabrizzi et al. (2005), Moreno et al. (1997) as well as Niedzwiecki and Pecio (2008) reported a significantly lower bulk density in tilled horizons than in those under conservation systems or annual chisel plowing thus contrasting our finding, Ferreras et al. (2000) reported

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insignificant differences between the tillage techniques. As observed by several researchers (Logsdon et al., 1999; Miller et al., 1998; Green et al., 2003), soil loosening in the plough layer after primary tillage tended to decrease rb in the 1–6 cm layer. Soil reconsolidation due to post-tillage rainfall events and associated wetting and drying cycles (Green et al., 2003) lead to increased rb and this could be responsible for the increased value observed in the present study in the soil under CT. 3.2. Soil water content at various matric potentials Table 3 contains data on soil water content at various matric potentials. Analysis of these data showed that soil under NT retained significantly greater amount of water at most measured matric potentials than the one found under the other tillage treatments at all the studied soil depths. However, all the tillage treatments were found to be statistically at par in their water content held at pF 3.176 (1500 kPa) within 5–15 cm depth and at pF 2.845 (700 kPa), pF 3.0 (1000 kPa) and pF 3.176 (1500 kPa) matric potentials within 15–30 cm depth. The greater soil water content observed in soil under NT is in conformity with the findings of Hill et al. (1985), Chang and Lindwall (1989), Hammel (1989) and Brandt (1992). This could be attributed to better soil aggregation (Shukla et al., 2003). 3.3. van Genuchten parameters The fitted values of van Genuchten parameters, estimated sum of squares (SSQ) of the fitted values vs. the measured water contents before and after RETC refining (optimizing predicted parameters to constant value), as well as the measured and predicted saturated hydraulic conductivity are presented in Table 4. Nonlinear least-squares analysis indicated that soil water at saturation, us was significantly higher with NT and became progressively lower from NT to MT to CoT to CT soil at all the depths. Contrary to our findings, Evett et al. (1999) reported significantly lower us in NT than CT plots. In the case of alpha and n values however, the trend was not consistent. There were no significant differences seen among the tillage treatments in the measured and predicted Ks values at all the studied soil depths except at 15–30 cm depth where soils under CoT and CT had significantly (p = 0.05) higher values of predicted Ks. The SSQ values reflect the relative accuracy of the retention models in describing the measured data. Generally, as the value of SSQ (residual) becomes smaller, precision in fitting using the retention model (RETC) is considered higher (van Genuchten et al., 1992). Based on this, it was found that van Genuchten model with Mualem-based restriction successfully and with high precision described soil water retention under all the tillage techniques. However, it was observed that the model best predicted water retention in soil under MT treatment at 0–5 cm depth than in the one tilled otherwise while the best prediction at 5–15 and 15– 30 cm depths was found in soil under CT. Porebska et al. (2006) attributed the differences in values of van Genuchten parameters to the differences in their physical and chemical properties with the content of particle size fraction playing the greatest role. The values were within the range of values found for loamy soils by Yates et al. (1992) in their study of 36 soils and Evett et al. (1999). 3.4. Soil water retention curve Fig. 1 demonstrates the relationship among measured and fitted water content under the various tillage techniques at 0–5 (A), 5–15 (B) and 15–30 (C) cm soil depths. The solid lines shown in Fig. 1A–C are 1:1 lines denoting the location where the measured

Fig. 1. Relationships among measured vs. fitted soil water content under various tillage treatments: no-till (NT), minimum tilled (MT), contour tilled (CoT) and conventionally tilled (CT) fields and at soil depths: 0–5 (A), 5–15 (B) and 15–30 (C) cm depths.

and the estimated values are equal. It is apparent that irrespective of tillage technique and soil depth, the measured vs. estimated water content are, in general, close to the 1:1 line, indicating that the soil hydraulic parameters listed in Table 4 well described the water retention relationship under all the studied tillage techniques at all the soil depths. The relationship among pF soil matric potential and water content derived from both measured and fitted values at various soil depths under the differently tilled soil is presented in Fig. 2A and B, respectively. It is apparent from these figures that irrespective of tillage technique, the shape of the water retention curves of the studied soil was similar for both measured and fitted relationships. However, marked differences in the water retention at various pF were noted among the tillage techniques. The water contents in NT and MT plots, including the quickly permeating gravitational water (with pF range of 1.12 to 0.799), easily accessible water for plants in the large capillaries (pF 1.519–2.0), and capillary water accessible with difficulty (within pF 2.0– 3.176 kPa), significantly exceeded those of CoT and CT plots at all the depths. Similar observation was reported by Evett et al. (1999). Also in conformity with the findings in this study, Czyz and Dexter (2009) also reported greater water content in reduced tilled and NT

164

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Fig. 2. Relationships among pF soil matric potential (kPa) vs. soil water content derived from measured (A) and fitted (B) values under various tillage treatments: no-till (NT), minimum tilled (MT), contour tilled (CoT) and conventionally tilled (CT) fields and at soil depths: 0–5, 5–15 and 15–30 cm depths.

fields as well as at topsoil than in CT and the deeper layers. In disagreement with our findings, however, Khurshid et al. (2006) reported that soil under CT treatment had higher moisture content than MT and NT treatments. The water content was found to decrease very slowly and almost unnoticeable under all the treatments and at the various depths in the range of pF 0–1.0 kPa for the measured relationship and almost 0.5 to 1.25 kPa pF range for the fitted relationship. More increased but gentle drop off of water content was observed as the pF values increased to 1.5 kPa followed by steeper drop off concomitant to increase in pF up to pF value of 3.2 kPa. NT soils had a less steep drop off of water content as tension increased. The

higher water retention in soils under NT and MT could be associated with high organic matter of humus fraction which in one hand increases microporosity and consequently water retention (Reicosky, 2005; Hudson, 1994). 3.5. Pore size and pore size distribution Table 5 contains data on two tillage treatments paired t-test for pore size distribution within varying ranges of matric potential at different depths while Fig. 3A–E presents information on the percent volume of pore size classes of the total (PVT) occupying 0.075 to 6.3 (holding quickly permeating gravitational water),

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Table 5 Tillage treatments paired t-test for pore size classes at various soil depths distributed within different ranges of soil matric potentials. Pairs of treatments

0–6.3 kPa MD (mm)

0–5 cm depth NT–MT NT–CoT NT–CT MT–CoT MT–CT CoT–CT 5–15 cm depth NT–MT NT–CoT NT–CT MT–CoT MT–CT CoT–CT 15–30 cm depth NT–MT NT–CoT NT–CT MT–CoT MT–CT CoT–CT

6.3–33 kPa

33–100 kPa

100–1500 kPa

>1500 kPa

SE

MD (mm)

SE

MD (mm)

SE

MD (mm)

SE

MD (mm)

SE

11.66ns 55.85ns 77.66ns 64.18ns 65.99ns 1.81ns

10.44 54.23 56.37 44.15 46.23 2.08

2.311** 2.473ns 2.787ns 2.146ns 2.459ns 0.314**

0.0140 1.4516 1.4825 1.4475 1.4782 0.0309

0.075* 0.007ns 0.171** 0.182* 0.346** 0.164**

0.0575 0.0078 0.0169 0.0653 0.0744 0.0091

0.356** 0.051** 0.412** 1.042** 0.045** 0.432**

0.0334 0.0142 0.0314 0.0417 0.0228 0.0403

0.012** 0.022** 0.025** 0.010** 0.015** 0.006**

0.0021 0.0032 0.0041 0.0013 0.0023 0.0011

56.94ns 18.54ns 65.42ns 38.40ns 8.48ns 46.88ns

37.19 10.55 40.14 26.61 2.95 29.56

4.936ns 3.294ns 3.811ns 3.284ns 1.467** 2.034ns

2.4673 1.8405 2.3671 1.6829 0.2475 1.5353

0.310* 0.254* 0.724** 0.078* 0.484** 0.562**

0.1174 0.0980 0.2019 0.0274 0.1043 0.1317

0.421** 0.040** 0.332** 0.441** 0.113** 0.357**

0.0428 0.0073 0.0243 0.0466 0.0217 0.0288

0.006** 0.012** 0.018** 0.006** 0.015** 0.010**

0.0013 0.0020 0.0036 0.0007 0.0025 0.0018

48.00ns 14.50ns 63.21ns 39.50ns 15.20ns 48.70ns

29.58 8.04 36.54 21.51 6.99 28.50

2.927ns 1.993ns 2.894ns 1.911ns 1.414ns 2.265ns

1.4894 1.1068 1.7041 0.9985 1.0808 1.4137

0.033* 0.338** 0.489** 0.212** 0.410** 0.243**

0.0438 0.0727 0.1171 0.0323 0.0867 0.0673

0.061** 0.088** 0.606** 0.017** 0.606** 0.584**

0.0185 0.0070 0.0460 0.0117 0.0320 0.0405

0.016** 0.003** 0.001ns 0.018** 0.016** 0.020**

0.0029 0.0008 0.0010 0.0038 0.0039 0.0001

MD, mean difference; SE, standard error; NT, no tillage; MT, minimum tillage; CoT, contour tillage; CT, conventional tillage. ** Significant at <0.01 level of probability. * Significant at 0.05 level of probability. ns, not significant at 0.05 level of probability.

6.3 to 33 (slowly permeating gravitational water), 33 to 100 (accessible water to plants), 100 to 1500 kPa (water accessible with difficulty) ranges of matric potential and above 1500 kPa (holding water not available to plants), respectively (Kalicka et al., 2008). The plots tilled differently did not significantly differ in the mean values of EPD occurring within 0.075 to 6.3 kPa soil matric potential at all the soil depths. However, Singh et al. (1996) and Ranjan et al. (2006) reported less volume of transmission pores distributed in this range of matric potential under direct drilling and no-till. The MT–NT and CT–CoT pairs showed significantly (p < 0.01) mean differences in EPD at 0–5 cm depth in the range 6.3 to 33 kPa with MT and CT being superior among the pairs. The pore size class occupies 6.9% in MT and 10.9% of the total in CT plots. The MT–CT pair also showed significant mean differences at 5–15 cm depth with CT having the highest mean value. In the range of 33 to 100 kPa matric potential, all the treatment pairs (except NT–CoT pair) showed significantly differences in mean EPD with soil under CT at 0–5 and NT at 5–15 and 15–30 cm depths distinguished by higher values. The PVT with the treatment was 6.0 and 13.8% at the respective depths. The CT plots had significantly (p < 0.01) higher mean values of EPD at 0–5 and 15–30 cm depths in all treatment pair combinations in the range of 100 to 1500 kPa. At 5–15 cm depth however, soil under MT had significantly higher mean value. Corresponding PVT of 11.0 and 12.5% were recorded at 0– 5 and 15–30 cm depths, respectively and 7.5% at 5–15 cm depth. Above 1500 kPa, soil under CoT was found to have significantly higher EPD mean value (0.061 mm) at 0–5 cm depth. The mean value was significantly higher in soil under CoT and CT at 5– 15 cm depth (0.059 mm) while the value was higher with soil under NT at 15–30 cm depth. However, the PVT was found to be greater with soil under NT at 0–5 cm depth (84.2%) and with MT at 5–15 cm depth (83.2%). Soil under CT maintained the highest PVT (68.9%) at 15–30 cm depth. Pore-size classes varied inconsistently with depth and in various matric potential ranges in the tillage treatments.

However, CT plots showed predominantly greater EPD at most of the depths and potential ranges. 3.6. Soil water content at inflection point and soil physical quality index Table 6 contains data on water content at inflection point (uINFL) and soil physical quality index (S). As seen from this table the tillage treatments exerted insignificant influence on water content at inflection point at 0–5 and 5–15 cm depths but had significant influence on the value at 15–30 cm depth. The CoT plots had higher uINFL at all the depths although the treatment had similar effect on

Table 6 Effect of tillage techniques on soil water content at inflection point and soil physical quality index. Tillage treatments 0–5 cm depth NT MT CoT CT Critical range 5–15 cm depth NT MT CoT CT Critical range 15–30 cm depth NT MT CoT CT Critical range

uINFL

S

0.3338a 0.3360a 0.3379a 0.3314a 0.01154

0.0191a 0.0176ba 0.0169bc 0.0156c 0.00174

0.3324a 0.3377a 0.3378a 0.3342a 0.00946

0.0691a 0.0690a 0.0680ba 0.0662b 0.00202

0.3305b 0.3350ba 0.3410a 0.3326ba 0.00873

0.0654ba 0.0660a 0.0659a 0.0639b 0.00174

NT, no tillage; MT, minimum tillage; CoT, contour tillage; CT, conventional tillage; uINFL, water content at inflection point; S, soil physical quality index. Means with the same letter in a column are not significantly different at 5% level of probability using Duncan’s Multiple Range Test (DMRT).

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Fig. 3. Volume fraction of total porosity occupied by pore size classes within various ranges of soil matric potentials: A (0.075 to 6.3 kPa range), B (6.3 to 33 kPa), C (33 to 100 kPa), D (100 to 1500 kPa) and E (above 1500 kPa) no-till (NT), minimum tilled (MT), contour tilled (CoT) and conventionally tilled (CT) fields.

the parameter to that of MT and CT treatments but different from NT. The range of S values at 0–5 cm was from 0.0191 to 0.0156 with NT plots having the highest value. The S mean values were also significantly higher with NT and MT plots and lower in CT plots at 5–15 cm depths with the range being 0.0662–0.0691. The statistically highest S value in the range of 0.0639–0.0660 was recorded in MT and CoT plots at 15–30 cm depth. About 7.9, 11.5 and 18.3% reduction in soil physical quality was observed at 0– 5 cm depth due to imposition of MT, CoT and CT treatment, respectively when compared with NT. About 0.1, 1.6 and 4.2% reduction in the physical quality was noted at 5–15 cm depths under the respective treatments. At 15–30 cm depth however, 1.9

and 1.8% increase and 2.3% reduction in the soil physical quality was observed under MT, CoT and CT treatments, respectively when compared with NT. According to Dexter (2004) and Dexter and Czyz (2007), boundary between soils with good and poor soil structural quality occurs at value of approximately S = 0.035. Based on this, the top 0–5 cm depths was in very poor structural condition (<0.020) which could be attributed to presence non-complexed organic carbon and readily dispersible clay (Czyz and Dexter, 2008; Dexter and Czyz, 2000; Gate et al., 2004). The 5–15 and 15–30 cm depths of the soil were characterized by very good structural quality irrespective of the tillage treatment.

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4. Conclusion The effect of tillage techniques on soil water retention and transmission, pore size distribution and soil physical quality in Samaru-Zaria, Nigeria was evaluated in a field experiment in 2010 and 2011 wet seasons using RETC computer model. The soil is characterized by loamy texture at all the studied depths. The soil under NT and CT were statistically equivalent but distinguished by significantly higher bulk density values than the other treatments. Soil water at saturation, us was significantly higher with NT and became progressively lower from NT to MT to CoT to CT soil at all the depths. In the case of alpha and n values however, the trend was not consistent. The soil under NT was distinguished by the highest amount of water of varying degree of usefulness to plants retained at all the studied ranges of soil matric potential. The influence of the tillage techniques on equivalent pore diameter and volume fraction of total porosity inconsistently varied with soil depths and within various ranges of matric potential. However, CT plots showed predominantly greater EPD at most of the depths and potential ranges. The soil under NT depicted the best soil physical quality at 0–5 and 5–15 cm depths while MT and CoT were distinguished by the best quality at 15–30 cm depth as indicated by the high soil physical quality index. Of all the tillage treatments, soil subjected to CT had comparatively poorer physical quality at all the depths. About 7.9, 11.5 and 18.3% reduction in soil physical quality at 0– 5 cm depth and 0.1, 1.6 and 4.2% reduction at 5–15 cm depth were observed due to imposition of MT, CoT and CT treatment, respectively when compared with NT. At 15–30 cm depth however, 1.9 and 1.8% increase and 2.3% reduction in the soil physical quality was observed under MT, CoT and CT treatments, respectively when compared with NT. The use of RETC model predicted with high precision the hydraulic properties of the studied soil under the various tillage techniques and soil depths indicating its validity in the determination of hydraulic properties of such soil type. Further research of this type could lead to more refined soil and management strategies for improving environmental quality and sustain productivity. Acknowledgments This research was approved for execution and funded by the Institute for Agricultural Research (I.A.R.), Ahmadu Bello University, Zaria, Nigeria. The authors wish to acknowledge with thanks the support provided by the Director of the Institute, the Farming System Programme Leader, and the Technologist of the Soil Science Department, Ahmadu Bello University, Zaria. References Anderson, J.M., Ingram, J.S.I., 1993. Tropical Soil Biology and Fertility. A Handbook of Methods, 2nd ed. CAB International, Wallingford, UK, p. 221. Berkowitz, B., Bear, J., Braester, C., 1988. Continuum model for contaminant transport in fractured porous formations. Water Resources Research 24, 1225–1236. Bibby, R., 1981. Mass transport of solutes in dual-porosity media. Water Resources Research 17, 1075–1081. Brandt, S.A., 1992. Zero vs. conventional tillage and their effects on crop yield and soil moisture. Canadian Journal of Plant Science 72, 679–688. Brooks, R.H., Corey, A.T., 1964. Hydraulic properties of porous media. Hydrology Paper, vol. 3. Colorado State University, Fort Collins, CO, pp. 22–27. Bruggeman, A.C., Mostaghimi, S., 1991. Simulation of preferential flow and solute transport using an efficient finite element model. In: Gish, T.J., Shirmohamadi, A. (Eds.), Preferential Flow. American Society of Agricultural Engineers, St. Joseph, MI, pp. 244–255. Burdine, N.T., 1953. Relative permeability calculation from pore-size distribution data. Transactions of the American Institute of Mining, Metallurgical and Petroleum Engineers 198, 71–77.

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