Assessment of tillage effects on soil quality of pastures in South Africa with indexing methods.
The soil quality concept receives much attention worldwide and it is applied to different agricultural systems or natural ecosystems. Soil quality is defined by Karlen et al. (1997) as 'the fitness of a specific kind of soil, to function within its capacity and within natural or managed ecosystem boundaries, to sustain plant and animal productivity, maintain or enhance water and air quality, and support human health and habitation' or simply 'the capacity of the soil to function'. Even though soil quality is sometimes viewed as a nebulous concept (Wilson et al. 2008), it is widely adopted because it encompasses the physical, chemical and biological processes in soil (Karlen et al. 1999). Soil is a medium in which a complex interrelationship between the three groups of processes exists (Bone et al. 2010). Plants rely on these processes to sustain productivity, because the services and functions provided by soil include cycling of nutrients and soil organic matter (SOM), partitioning of water, buffering and the provision of physical support (Swanepoel 2014). Cycling of SOM, nitrogen (N) and other nutrients in soil depends on microbially mediated processes, which subsequently supply plant-available forms of nutrients (Parfitt et al. 2010). These processes make soil quality dynamic and they are a product of the inherent characteristics of a soil and its capacity to react to the environment and management inputs (McBratney et al. 2014). Soil quality of agricultural systems can therefore change through time because of management operations. Continued soil tillage and deep tillage are management operations that usually have a profound negative impact on soil quality (Karlen et al. 1994, 2013; Bissett et al. 2011; Sun et al. 2011). This is because the environment for microbial activity differs within only a few centimetres of soil along the depth profile, because of stratification of SOM and its related parameters, nutrients, texture and soil pores (gaseous exchange and water availability) (Curci et al. 1997). When these different horizons are disrupted and modified by tillage, changes in the microbial activity and soil quality are anticipated.
In the dairy production systems of the southern Cape region of South Africa, several tillage or no-till implements are used to establish or over-sow pastures (Botha et al. 2008). In this region, perennial grass systems are kikuyu-based (Pennisetum clandestinum), with cultivation in pure swards, or more commonly, by over-sowing with different ryegrass species (Lolium spp.) or varieties, using mulchers and minimum-till seed drills (van der Coif 2011), tined implements, rotovators or conventional disc ploughs. Pure ryegrass swards are established after application of herbicides to eradicate the self-sown plants of previous growing seasons (Swanepoel et al. 2014a). The management goals of these pasture systems in the southern Cape region are to increase pasture productivity, to ensure proper recycling of manure and other waste products, and to enhance environmental protection (Swanepoel 2014). Soil quality is site-specific and linked to services performed by a soil intended for a specific land-use (Karlen et al. 2003; Sojka et al. 2003). The soil quality of the pasture systems in the southern Cape region, however, has not been assessed. Therefore, the response to different land-use or tillage procedures that are aimed at specific management goals has not been identified. Soil quality assessment could aid in interpretation of the functions or soil services, thereby aiding the monitoring of management outcomes and assisting the process of prioritising management actions (Karlen and Stott 1994; Andrews et al. 2002). Subsequently, problem areas arising from superficial management practices could be detected and addressed.
It is important to evaluate these tillage practices at a scale inclusive of variation in topography, pedogenic soil characteristics and factors influenced by the land manager or farmer. An assessment framework for soil quality will be useful when it could be across different topographies and soil characteristics. This study tested a soil quality index, which was proposed by Swanepoel et al. (20146) for the pastures of the southern Cape region, on five different tillage practices and compared it with the soil management assessment framework (SMAF), which was developed by Andrews et al. (2004). This study aimed at understanding the effects of tillage on soil quality, and establishing a comparison between the two soil quality assessment methods.
Materials and methods
Study region and soils
The coastal region surveyed is ~6000 [km.sup.2] and extends from Stormsvlei (34[degrees]05'05"S, 20[degrees]05'08"E) in the Western Cape Province of South Africa to the Van Stadens River (33[degrees]54'33"S, 25[degrees]11'50"E) in the Eastern Cape Province (altitude 0-300 m above sea level). This region has a temperate climate with daily maximum temperatures that vary between 18[degrees]C and 25[degrees]C, and minimum temperatures between 7[degrees]C and 15[degrees]C (from winter to summer), and with no frost days (ARC-1SCW 2014). Rain falls throughout the year and varies between ~700 and 1000 mm, depending on the distance from the Indian Ocean to the south and the Tsitsikamma, Outeniqua or Langeberg mountain ranges to the north.
The region's dominant soils are mainly well-sorted sands or sandy loams in the top 200-300 mm layer (ARC-ISCW 2006) and are characterised in the podzol and duplex soil groups (Soil Classification Working Group 1991; IUSS Working Group 2006) otherwise known as Spodosols and Alfisols, respectively (Soil Survey Staff 2003). Sampling of pastures made no distinction between soil groups or families, but the indexing methods used considered soil textural classes (see below: Assessment of soil quality). Cultivated pastures are usually established on slopes of 1-3%, although these could range from 0% to 10%.
Pastures on 142 well-managed, commercial dairy farms were identified for the assessment, with the aid of extension officers and farmer co-operators. The management history of the pastures needed to be available on record, and had to span at least the past 5 years. Management had to be consistent in order to be considered for the assessment. According to farmer interviews, 68 of the assessed pastures were consistently managed under the specific tillage practice for the past 5-10 years, and 74 of the assessed pastures were managed consistently for >10 years under the current tillage practice. Management practices of the pastures before the past 5 years of consistent management were considered to have had no artefact effects on the current system. The tillage practices were defined as follows:
(1) No disturbance (ND): pure kikuyu grass pasture without any soil disturbance or over-sowing practice (n = 20).
(2) Kikuyu-ryegrass pasture (KR): kikuyu grass-based pasture over-sown annually with annual or perennial ryegrass using a minimum-till seed drill after the kikuyu base was mulched to ground level. This is the most common practice on dairy pasture farms in the region (n = 62).
(3) Herbicide-treated pasture (HT): pure annual ryegrass established with minimum-tillage methods, following eradication of the summer self-sown crop or weeds by herbicide treatment (n = 29).
(4) Shallow tillage (ST): a kikuyu-based pasture reinforced with annual or perennial ryegrass by means of shallow tillage (<150 mm deep) (n = 16).
(5) Deep tillage (DT): conventionally tilled kikuyu-ryegrass pasture soil using an offset disc plough (n = 15).
The number of assessments per tillage practice was not equal, but represented the approximate total area covered under those practices.
Soil samples, representative of the whole field, were collected from July to November 2012. At least 20 cores per pasture were collected from 0-100 mm depth by using a 70-mm soil auger, subsequently composited, placed in plastic bags and stored at 6[degrees]-8[degrees]C until biological analyses could be conducted. Each sample was considered a replicate for the applicable tillage practice, and the study design was therefore completely randomised with five tillage practices and unbalanced sampling numbers per practice.
Particle-size distribution was determined using the hydrometer method (Day 1965). From these data, water-holding capacity (WHC) was determined by the soil-water characteristics model developed by Saxton and Rawls (2006). Bulk density was measured by taking intact soil samples with a double cylinder of known core volume (diameter 70 mm, length 75 mm) (Blake 1965). Penetration resistance was measured with an electronic penetrometer (Geotron hand penetrometer model P5 LT400, loadcell type SUB-G-200, error=0.03%; Geotron Systems, Potchefstroom, South Africa) by taking the mean of 20 readings per plot, measured at 10-mm sampling intervals. The cone base area of the penetrometer was 130 [mm.sup.2], the cone diameter 12.8 mm and the apex angle 30[degrees]. One of the limitations was that soil-water content at the time of sampling was not known, and this will be discussed accordingly.
Soil pH was determined in a 1 :2.5 soil: potassium chloride (KC1) solution, and exchangeable acidity and base cations (calcium ([Ca.sup.2+]), magnesium ([Mg.sup.2+]), potassium ([K.sup.+]) and sodium ([Na.sup.+])) were determined through use of a KC1 and citric acid solution (Non-Affiliated Soil Analysis Work Committee 1990). Extractable phosphorus (P) levels were determined with the citric acid method, boron (B) with the hot water method, copper (Cu), manganese (Mn), and zinc (Zn) with the di-ammonium EDTA method, and cation exchange capacity (CEC) with the ammonium acetate method. The sodium adsorption ratio (SAR) was calculated after determination of Na, Ca and Mg in saturation extracts.
Soil biological analyses included total SOM by gravimetric measurement of carbon dioxide (C[O.sub.2]) loss-on-ignition at 550[degrees]C for 3 h (Broadbent 1965), soil organic C (SOC) measured by the Walkley-Black procedure (Nelson and Sommers 1982), total N by the Kjeldahl digestion procedure (Bremner 1960), active C by dichromate digestion and colourimetric measurement of non-reduced Mn at 550 nm (Weil et al. 2003), and potentially mineralisable N (PMN) by the anaerobic incubation procedure (Drinkwater et al. 1996). Four enzymes ((3-glucosidasc, urease, alkaline and acid phosphatase) were assayed by standard procedures (Kandeler and Gerber 1988; Dick et al. 1996).
Community level physiological profiles were assessed by diluting soil samples in sterile distilled water (1:3000) (Buyer and Drinkwater 1997) and inoculated into the Biolog EcoPlates[TM] (Biolog Inc., Hayward, CA, USA) containing 31 C sources and a control well, in triplicate. This allows for the recovery of several types of bacteria and retains abundant microbes, while fast-growing competitors are eliminated (De Fede et al. 2001). The plates were incubated at 28[degrees]C. Respiration by microbes reduced the tetrazolium dye within each EcoPlate well, thereby causing the colour of the well to change, which could be measured spectrophotometrically. This was done twice daily over 7 days, at 590 nm, to determine average well colour development (Winding and Hendriksen 1997). The optical density values obtained from each Biolog MicroPlate were analysed using the average well colour development technique and standardised by blanking the absorbance values for the wells as described by Garland (1996). Any negative values were converted to zero and any variance in the inoculum density was accounted for by dividing the absorbance of each well by the average absorbance for the whole MicroPlate, giving the standardised optical density. Instead of using the absolute values, standardised patterns were subsequently compared (Habig 2003). Soil microbial diversity was determined by using the Shannon-Weaver (H') and Evenness (E) indices as described by Magurran (1988).
Assessment of soil quality
The SMAF, developed in the USA by Andrews et al. (2004), was used to assess soil quality of the tillage practices and compared with the locally developed soil quality index for pastures (SQIP) in the southern Cape region, as comprehensively discussed by Swanepoel et al. (2014b).
For the SMAF, nine soil quality indicators (bulk density, WHC, pH, exchangeable K, extractable P, SAR, SOC, PMN and [beta]-glucosidase) were used to calculate soil quality status of each pasture. In the SMAF, [pH.sub.water] had to be used, and [pH.sub.KCl] was converted to [pH.sub.water] via the regression [pH.sub.water]= 0.702 x [pH.sub.KCl]] + 2.380 (r = 87%, P<0.05). The second SOM class was chosen because of the relatively high levels of SOM of soils in the region, and texture factor classes were used according to the particle-size distribution data. The crop code for fescue was used, because a crop code for the current system is not available in the indexing method. Fescue and ryegrass are both temperate [C.sub.3] grass species, and kikuyu--ryegrass pasture systems are expected to act in a manner similar to established fescue pastures. The abovementioned minimum dataset (MDS) of indicators was then transformed into unitless scores by using scoring curves consisting of either a simple algorithm, or a logical statement with a different algorithm. These algorithms are set out in a paper by Andrews et al. (2004), and they reflect the performance of the services that the soil provides. The indicator scores of the MDS generated in the previous step were subsequently integrated into an additive index, which provides a score expressed in terms of the percentage of the maximum possible soil quality. Sector scores for soil quality indices were also calculated. These comprise a combination of the soil quality indicators within a certain sector (physical, chemical, fertility or biological sectors), each of which was transformed into a simple additive score expressed in percentage. Bulk density and WHC were used to quantify soil physical quality. For soil chemical quality, extractable P, exchangeable K, pH and SAR levels were used to calculate the score. For soil fertility, extractable P and exchangeable K, and for soil biological quality, PMN, SOC and [beta]-glucosidase, were each, respectively, integrated into sector indices.
The SQIP is a simple indexing method developed for intensive dairy-pasture systems in the southern Cape region of South Africa, and the methodology used to develop the index is discussed in detail by Swanepoel et al. (20146). The MDS consisted of seven soil quality indicators and it was identified statistically from a total dataset of 30 indicators. This index involves a series of scoring functions and subsequently incorporated penetration resistance, gravel content and WHC as physical indicators, exchangeable acidity, extractable P and exchangeable Mn as chemical indicators, and SOM as a biological indicator. These indicators were differing weights in the index and were ranked in decreasing order as follows: extractable P > gravel content > WHC > exchangeable acidity > SOM = penetration resistance > exchangeable Mn. The output was a simple score expressed as a percentage of the maximum possible soil quality according to the performance of the services that the soil provides.
The methods used to develop the SMAF and SQIP are comparable; however, the agricultural systems used when these indexing methods were developed differed. This may be an important consideration because soil quality is specific to crop and land-use (Karlen et al. 2003; Sojka et al. 2003).
An accumulated analysis of deviance was used to test for differences between treatments of soil quality indicators because residuals were not normally distributed. A gamma distribution was used in the analysis because the frequency distribution of the soil quality indicators was skewed. The reciprocal link function was used to establish the connection between the linear predictor ([eta]) and the mean of the distribution ([mu]) so that [eta] = 1/[mu] (Venables and Dichmont 2004). Note that although the reciprocal link function does not map the range of the mean into the unbounded natural range of the linear predictor, and the values of standard error of the mean (s.e.m.), variance and least significant difference (l.s.d.) are approximate, these estimates should be adequate for the situation. The s.e.m. values are based on the residual deviance. Means were separated with Student's t-test conducted at P= 0.05 and pairwise differences were established between the tillage treatments. The Fisher's l.s.d. test was based on the criterion of P<0.05. However, the residuals of indicator scores generated by the SMAF and SQIP were normally distributed; therefore, an accumulated analysis of variance (ANOVA) of an unbalanced design was used to test for differences between treatments. Means were separated with a Student's t-test, conducted at a P=0.05. The program Genstat was used to perform statistical analyses (Payne et al. 2012).
Results and discussion
Tillage effects on soil physical quality
The tillage practices presented similar (P > 0.05) gravel, silt and sand contents, but significant (P < 0.05) variation in surface clay content was observed for soils subjected to treatment DT for at least the past 5 years (Table 1). The soils from the other tillage treatments may have been deep-tilled prior to past 5 years; however, historical tillage, if any, evidently had no effect (P > 0.05) on soil surface texture.
Penetration resistance and bulk density are measures of soil physical resistance or compaction, and could affect the physical, chemical and biological processes in soil. These dynamic physical indicators could respond to management (Houlbrooke et al. 1997). Lower values are favoured because soil compaction limits plant production by creating conditions that impede root growth and reduce the supply of water, oxygen and dissolved nutrients (Diaz-Zorita et al. 2002). Mean bulk density among the tillage sites varied from very open (1011 kg [m.sup.-3]) in the ND treatment to very compact (1504 kg [m.sup.-3]) in the DT treatment (Table 1). Bulk density of DT did not differ (P > 0.05) from HT, but was higher (P < 0.05) than that of the other treatments. Similar findings have been described for the eastern districts of the region (Milne 2002). The ST treatment had an intermediate bulk density, followed by KR and then ND, which had the lowest (P < 0.05). A similar pattern was observed for penetration resistance, with DT having the highest (P < 0.05) resistance and the ND and KR treatments the lowest (P < 0.05). Soil-water content, however, varied between sites and could be responsible for some of the variation between treatments.
The pedogenic characteristics of the dominant soils in the southern Cape region are attributed to the soils being prone to compaction, because the particle-size distribution is typically high in fine sand and low in clay (Swanepoel et al. 2013). The use of tillage to alleviate soil compaction is commonly described in the literature (Carter 1990; Taboada et al. 1998; Diaz-Zorita et al. 2002) and is largely a short-term solution. The opposite effect has been noted in similar studies of annually tilled pastures (Lai 1993; So et al. 2009). Continuous deep tillage over a period of [greater than or equal to] 5 years had an adverse impact on soil compaction, and conversely, no soil disturbance had a positive impact. Reducing tillage seems to be the best management option to alleviate compaction of the soils in the southern Cape region.
Measures of soil compaction are often included in minimum datasets of soil quality assessments (Voorhees 1983; Karlen and Stott 1994; Arshad et al. 1996; Raper et al. 2000; Andrews et al. 2004; Stott et al. 2013). Arshad et al. (1996) suggested that bulk density, specifically of the topsoil, could be used as a soil quality indicator. However, this does not take into account subsoil compaction or impeding plough layers, which could have substantial adverse effects on plant productivity (Logsdon and Karlen 2004). In the present study, bulk density and penetration resistance did not differ (P > 0.05) between treatments at depths of 100-200 and 200-300 mm; therefore, sampling only the 0-100 mm depth is appropriate to investigate soil physical quality.
The WHC did not differ (P>0.05) between treatments and was low because of the sandy texture, but was within the range normally observed in the soils of the region (Swanepoel et al. 2013).
The sector assessment of SMAF used to assess soil physical quality considered bulk density and WHC as physical indicators (Fig. 1). Sector scores were calculated for soil physical quality from bulk density and WHC. The KR treatment did not differ (P > 0.05) from the ND treatment, and had higher (P < 0.05) soil physical quality than the other treatments. This corresponded with the findings of a study by Swanepoel et al. (2014a), who reported that pastures similar to the KR and ND treatments had the highest annual pasture production compared with treatments similar to HT, ST and DT. The DT treatment had the poorest (P < 0.05) physical quality, and HT and ST intermediate soil physical quality. Effects of disruption of the soil surface environment caused by tillage were evident in the altered clay content at 0-100 mm depth. Therefore, this could, also affect soil chemical quality (CEC and leaching potential of nutrients) and soil biological quality (disruption of SOM equilibrium of stabilised and aggregate-protected SOM, gaseous exchange and partitioning of water) (Curci et al. 1997).
Tillage effects on soil chemical quality
Soil pH and exchangeable acidity were not affected (P > 0.05) by tillage; however, exchangeable Ca and Mg, which are mostly a function of lime application to alleviate soil acidity, were affected (P < 0.05) (Table 1). The DT treatment had lower (P < 0.05) exchangeable Ca and Mg concentrations than the ND and KR treatments, and HT had lower (P < 0.05) exchangeable Mg levels than ND and KR. A similar pattern was observed for exchangeable K and Na, and extractable P, Cu, Zn and B, except that the KR treatment had a B level not significantly (P > 0.05) different from HT. Only the HT treatment had lower (P < 0.05) exchangeable Mn levels than the other tillage treatments. The CEC remained unaffected (P > 0.05) by tillage.
Tillage effects on soil chemical quality would result from mixing the highly stratified topsoil layers (Swanepoel et al. 2014c) with the deeper layers that are relatively nutrient-poor, rather than quantitative changes in the levels of nutrients. This would explain the lower (P < 0.05) levels of exchangeable Ca, Mg, K and Na as well as extractable P, Cu, Zn, and B in the DT treatment than the treatments under permanent pasture and reduced tillage (ND and KR), because DT has been tilled annually for at least 5 years. However, the HT treatment also showed lower (P < 0.05) levels of nutrients (exchangeable Mg and K and extractable P, Cu, Zn, Mn and B), even though soils under this treatment were not tilled. Since the soils of the HT and DT treatments were sown exclusively with annual winter-growing grasses, they likely received less fertilisation over the course of a year, resulting in lower nutrient status. These findings concur with those of Carter and Rennie (1982), who found increased levels of P and K in the top 80 mm of soil under reduced tillage compared with conventional tillage.
The sector assessment of SMAF on soil chemical quality reflected extractable P, exchangeable K, pH and SAR, and soil fertility only extractable P and exchangeable K. There were no differences (P > 0.05) between treatments for both the soil chemical quality and fertility assessments (Fig. 2).
Although tillage effects did not influence the soil chemical quality of the soil according to the chemical sector of SMAF, increases in certain nutrients, especially in no-till pastures (Table 1), emphasise the need for regular monitoring of soil nutrient status, which should play a central role in fertility management of pastures in dairy production systems (Beare et al. 2005). This should prevent the build-up of nutrients to toxic levels and prevent unnecessary costs incurred by over-fertilisation. Adverse nutrient build-up was noted in the region by Swanepoel (2014) when no-till kikuyu--ryegrass pasture was compared with soil in its native state.
Tillage effects on soil biological quality
Soil organic matter ranged from 3.9% to 8.6% and SOC from 2.3% to 6.3% (Table 1). Predictably, the highest (P < 0.05) value for SOC was recorded for the ND treatment, because pure kikuyu builds up a matt in the topsoil through time, and could accumulate large proportions of SOM (Haynes et al. 2003). This corresponded to the results for SOM, but the SOM in the ND treatment did not differ (P > 0.05) from that of the ST treatment. Total N was highest (Pc 0.05) in the ND treatment, followed by KR and ST; the HT and DT treatments were similar (P > 0.05), with lower (P < 0.05) total N concentration than the other treatments. The mineralisation of N was unaffected by tillage (P < 0.05), contrary to the findings of Carter and Rennie (1982). Total N, SOM and SOC are usually well correlated, because most of the N is bound in organic matter matrices (Barnett et al. 2014; Orgill et al. 2014). These tillage effects on SOM and its related indicators agree with the findings of Potter (2006) and Powlson et al. (2012). However, Luo et al. (2010) showed, in a wide-ranging meta-analysis, that under these no-tillage situations, soil C became highly stratified in the surface layers, although there was no significant difference (P > 0.05) between tilled and no-tillage systems when the top 400 mm of soil was considered. The soils in the southern Cape region are highly stratified, with organic-matter-related indicators in the top 100 mm (Swanepoel et al. 2014c). This stresses the importance of the soil surface layer as an interface between soil and the external environment for additions of water, oxygen and nutrients. However, it does not mean that the overall organic matter stocks of the system are increased, but should rather ensure a soil surface able to accept and filter organic and inorganic materials, regulate and partition water, maintain biological integrity, and provide physical support and stability, which are important functions in pasture systems (Swanepoel 2014). This should be especially helpful in mitigating inherent constraints of the soil physical component, such as the well-sorted, fine sandy texture rendering the soil more prone to compaction and the low inherent WHC. This effect was evident from the high bulk density and penetration resistance of the DT treatment, with lower (P < 0.05) SOC levels than the ND and KR treatments. However, the WHC was not affected by tillage, and the effects of differences in SOM and SOC could not be clearly established.
Determination of enzyme activities may be a valuable tool in interpreting nutrient cycles and SOM dynamics, processes essential for plant nutrition and production. For the enzymes [beta]-glucosidase, urease and alkaline phosphatase, the DT and HT treatments had the lowest (P < 0.05) activities or similar (P > 0.05) to the lowest. The inverse was observed for acid phosphatase, which had the highest (P < 0.05) activity in the DT and HT treatments. Acid phosphatase is reported as exclusively exuded by plant roots in cases of P deficiency to increase availability of phosphate in the soil (Tadano and Sakai 1991; Gilbert et al. 1999). The increased activities of [beta]-glucosidase, urease and alkaline phosphatase in reduced tillage compared with conventional tillage are in agreement with findings of many studies (Carter and Rennie 1982; Curci et al. 1997; Bandick and Dick 1999; Roscoe et al. 2000; Ekenler and Tabatabai 2003; Green et al. 2007).
Curci et al. (1997) also found differences in [beta]-glucosidase activity between shallow and deep tillage treatments but, in contrast to this study, no differences (P > 0.05) in acid or alkaline phosphatase activities between shallow and deep tillage (and scarification) treatments. Green et al. (2007) found similar results for [beta]-glucosidase, and alkaline and acid phosphatase activities. These studies stressed the importance of depth of tillage and the impact it may have on distribution of nutrients and organic C turnover, which is heightened by the differences not only in [beta]-glucosidase activity but also in acid and alkaline phosphatase activities found in the soils of the southern Cape region. Green et al. (2007) and Ekenler and Tabatabai (2003) reported significant differences in the distribution of enzyme activities with depth between tillage and reduced-tillage practices. Future investigation is warranted of the enzymatic activities at greater depths than the top 100 mm, as well as the effectiveness of enzymes in the monitoring of changes in biological quality because of tillage practices.
According to the assessment by the SMAF, soil disturbance had the greatest influence on the biological quality of the soil (Fig. 3). The soil biological quality of the ND treatment did not differ (P > 0.05) from the KR treatment, but was higher (P < 0.05) than that of the other treatments. This was followed by the HT and ST treatments, although the latter two treatments did not differ (P > 0.05) from KR. The DT treatment had a significantly lower (P < 0.05) soil biological quality assessment than ND and KR. To increase the biological quality of soil, it should not be disturbed, or reduced tillage practices should at least be followed. This is also confirmed by soil microbial diversity indices and C-source utilisation profiles. The type of crop present and tillage practice utilised strongly influenced the amount and availability of root exudates, consequently influencing the diversity and abundance of microbial species, as demonstrated by the Shannon-Weaver and Evenness indices (Table 2).
Species abundance ranged between 0.82 and 0.86, with a significant difference (P < 0.05) between ST, ND and KR. Microbial species diversity in the ST treatment (2.59) was lower (P < 0.05) than diversity in the ND (2.76) and KR (2.80) treatments. Microbial diversity in DT treatment (2.65) was also lower (P < 0.05) than in KR (2.80) treatment. The difference in microbial diversity between the treatments was brought about by the availability of various C sources from root exudates or SOM, and soil disturbance. Bacterial communities under the different practices utilised all of the C substrates as carbohydrates, carboxylic acids, amino acids, amines, phenolic compounds, or polymers (Table 3).
Carbohydrates were the most utilised, whereas amines and phenolic compounds were the least utilised. Bacterial communities under DT and ST utilised the most carbohydrates but the least phenolic compounds and amines, whereas communities under ND and KR utilised mostly carboxylic acids, polymers, and amines. Bacterial communities under different management practices utilised different individual C substrates among the main C source groups (Table 4).
D-Cellobiose was the most highly utilised carbohydrate under ST, along with N-acetyl-D-glucosamine, which showed higher utilisation (P < 0.05) under the ST treatment than the other treatments except DT, which was not significantly different (P > 0.05). D-Galactonic acid was most highly utilised under the DT treatment. Bacteria from all management practices showed a noticeable preference for D-galacturonic acid among the carboxylic acids. The HT treatment metabolised the greatest (P < 0.05) amounts of L-asparagine, although not significantly more (P > 0.05) than the ND and KR treatments. The HT treatment metabolised the greatest (P <0.05) amounts of L-thrconine.
Tillage effects on overall soil quality
Effects of tillage on overall soil quality are shown in Fig. 4 as measured by the SMAF and the locally developed SQIP. Both indexing systems showed that ND and KR had the highest (P < 0.05) soil quality, but SMAF showed that ST ranked closely with ND and KR (P > 0.05). The SQIP showed that HT ranked with ND and KR. Both indexing methods also showed that the DT treatment had the lowest (P < 0.05) soil quality. The SQ1P, however, showed that HT and ST did not differ (P > 0.05) from DT. Table 5 shows no significant (P > 0.05) correlation between results from SQIP and SMAF or any of the sector components, except for soil physical quality, which was weakly significant (P < 0.05, r = 0.21).
This therefore suggests that the outcomes of the two indices are significantly different. Since soil quality is site- and land-use specific, the SQIP would be the more suitable method to apply for assessment of soil quality because it was developed specifically for these pasture systems (Swanepoel et al. 2014b). Furthermore, when the results from Fig. 4 are compared with a study by Swanepoel et al. (2014a), where pasture productivity and botanical composition of these five tillage treatments were evaluated, it is evident that the SQIP would be better suited than SMAF to assess soil quality of pastures in the southern Cape region. The study by Swanepoel et al. (2014a) showed that the ND and KR treatments had the highest (P < 0.05) annual production, and this corresponds to the results from both SMAF and SQIP. The DT and ST treatments did not differ (P > 0.05) in annual pasture production, but was lower (P < 0.05) than results recorded in the ND and KR treatments. This result corresponds to SQIP only, since SMAF showed that the ST treatment had a soil quality index score similar (P > 0.05) to the highest (ND and KR). The HT treatment, however, had the lowest (P < 0.05) pasture production (also similar to ST), but when the seasonal productions were investigated, the production levels were merely a function of the time when the previous season's crop had been eradicated with herbicide, and the production levels for the rest of the year were similar (P > 0.05) to the ST and DT treatments.
Soil tillage affects soil conditions, which corresponds to yield because of rapid changes in mineralisation rates of organic matter and a faster nutrient-cycling rate (McIvor et al. 1995; Curci et al. 1997). Hence, it is important to maintain soil quality in order to sustain pasture production. The close link between pasture production and soil quality should therefore be reflected in the results from the soil quality index, in order to make accurate recommendations and to adapt management strategies for a beneficial outcome.
Although the study of effects of tillage practices on soil quality confirms the results of many former investigations on a global scale, it is the first to reflect the different outcomes of the locally developed SQIP compared with another method of soil quality assessment. Soil quality is site-specific and linked to services performed by a soil intended for a specific land-use (Karlen et al. 2003; Sojka et al. 2003); in this case, pastures used in dairy production systems. Because these pastures have specific management goals, the SQIP seems to provide a more accurate estimation of soil quality. However, other pasture systems with similar management goals could use the SQIP for accurate assessment of soil quality. Similar pasture systems are cultivated in various regions in the world, such as Australia (Fulkerson et al. 2010), New Zealand (Crush and Rowarth 2007) and South America (Chilibroste 2002).
Prolonged tillage has a profound impact on physical, chemical and biological processes in soil. Soil physical quality was found to be largely a function of the inherent pedogenic characteristics, but was heightened by soil disturbance. Reducing tillage is the best management option to alleviate compaction of the soils in the southern Cape region and to maintain soil physical quality. Tillage did not affect the soil chemical quality of the soil; however, elevated levels of certain nutrients such as P and Zn, especially in reduced-tillage pastures, underscore the fact that soil fertility management of pastures in dairy production systems should be prioritised. Effects of soil disturbance were most pronounced with regard to soil biological quality. Deep tillage resulted in the lowest soil biological quality, but shallow tillage or the use of herbicides to establish annual pastures also leads to low soil biological quality. No-till or minimum-tillage with a permanent kikuyu-based pasture remains the best management option to maintain soil biological quality. Because soil chemical quality was not affected by the treatments, the differences in soil quality between tillage practices were therefore mostly due to soil physical and biological quality. Different indices or assessment tools exist, but because soil quality is site- and land-use specific, any soil-quality assessment tool will not necessarily be adequate. The SMAF and the locally developed SQIP were used to assess the five different tillage treatments, and it is concluded that the SQIP is an appropriate tool to assess soil quality for high-input dairy pastures. SQIP could facilitate adaptive management by land managers, environmentalists, extension officers and policy makers to assess soil quality and enhance the understanding of processes affecting soil quality. The efficacy of the SQIP should, however, be further investigated.
Received 20 August 2014, accepted 24 November 2014, published online 26 March 2015
This research was funded and carried out by the Western Cape Department of Agriculture. Mr. Brian Zulu and his team of farm aids are acknowledged for their technical assistance and hard work in the collection of field data. We also thank the farmers who made their pastures available for sampling and the extension officers and farmer co-operators who assisted with the identification of suitable farms and fields. Mrs MF Smith from stats4science in Pretoria is sincerely thanked for her patience and assistance with the statistical analyses. We also acknowledge Dr Diane Stott for providing the SMAF in excel format in order to assess soil quality of the pastures in the southern Cape region. The linguistic editing of the manuscript by Ms Manuela Lovisa is highly appreciated.
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P. A Swanepoel (A,B,F), C. C. du Preez (C), P. R. Botha (B), H. A. Snyman (D), and J. Habig (E)
(A) Department of Agronomy, University of Stellenbosch, Private Bag XI, Matieland 7602, South Africa.
(B) Western Cape Department of Agriculture, Outeniqua Research Farm, PO Box 249, George 6530, South Africa.
(C) Department of Soil, Crop and Climate Sciences, University of the Free State, PO Box 339, Bloemfontein 9300, South Africa.
(D) Department of Animal, Wildlife and Grassland Sciences, University of the Free State, PO Box 339, Bloemfontein 9300, South Africa.
(E) Agricultural Research Council--Plant Protection Research Institute, Private Bag X134, Queenswood, Pretoria 0121, South Africa.
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Table 1. Means ([+ or -] s.e.m.) of soil quality indicators tested on five tillage treatments ND, No disturbance; KR, kikuyu-ryegrass; HT, herbicide-treated pasture; ST, shallow tillage; DT, deep tillage; WHC, water-holding capacity; EA, exchangeable acidity; SOM, soil organic matter; SOC, soil organic carbon; PMN, potentially mineralisable nitrogen. Within rows, means followed by the same letter are not significantly different (P>0.05) n: ND 20 KR 62 Physical indicators Clay (%) 15 [+ or -] 1.5b 15 [+ or -] 0.8b Silt (%) 26 [+ or -] 2.6 27 [+ or -] 1.5 Sand (%) 60 [+ or -] 5.5 59 [+ or -] 2.9 Gravel (%) 2.8 [+ or -] 1.0 2.4 [+ or -] 0.5 Bulk density (kg [m.sup.-3]) 1011 [+ or -] 35d 1144 [+ or -] 22c Penetration resistance (kPa) 1719 [+ or -] 181 bc 1574 [+ or -] 77c WHC (mm [m.sup.-1]) 134 [+ or -] 3.8 139 [+ or -] 2.1 Chemical indicators pH 5.70 [+ or -] 0.14 5.83 [+ or -] 0.08 EA (emol [kg.sup.-1]) 0.86 [+ or -] 0.25 0.52 [+ or -] 0.08 Ca (mg [kg.sup.-1]) 1848 [+ or -] 180a 1727 [+ or -] 93a Mg (mg [kg.sup.-1]) 546 [+ or -] 63a 410 [+ or -] 26b K (mg [kg.sup.-1]) 454 [+ or -] 79a 312 [+ or -] 30b Na (mg [kg.sup.-1]) 137 [+ or -] 25.7ab 129 [+ or -] 13.3ab CEC (emol [kg.sup.-1]) 14.9 [+ or -] 1.4 12.9 [+ or -] 0.7 P (mg [kg.sup.-1]) 272 [+ or -] 46a 221 [+ or -] 21a Cu (mg [kg.sup.-1]) 3.08 [+ or -] 0.44a 3.33 [+ or -] 0.26a Zn (mg [kg.sup.-1]) 25.5 [+ or -] 5.3a 28.1 [+ or -] 3.2a Mn (mg [kg.sup.-1]) 69.7 [+ or -] 15.6 a 61.7 [+ or -] 7.7 a B (mg [kg.sup.-1]) 0.79 [+ or -] 0.08a 0.68 [+ or -] 0.014ab Biological indicators SOM (%) 8.6 [+ or -] 1.8a 4.9 [+ or -] 0.6b SOC (%) 6.3 [+ or -] 0.7a 4.4 [+ or -] 0.3bc Active C (mg [kg.sup.-1]) 2679 [+ or -] 91a 2544 [+ or -] 48a Total N (%) 0.64 [+ or -] 0.05a 0.48 [+ or -] 0.02b PMN ([micro]g N [g.sup.-1] [week.sup.-1]) 34.1 [+ or -] 3.1 29.3 [+ or -] 1.5 (3-glucosidase ([micro]g [g.sup.-1] [h.sup.-1]) 4269 [+ or -] 583a 3905 [+ or -] 319ab Urease ([micro]g [g.sup.-1] [h.sup.-1]) 233.7 [+ or -] 30.1a 196.1 [+ or -] 15.1a Acid phosphatase ([micro]g [g.sup.-1] [h.sup.-1]) 1215 [+ or -] 39c 1384 [+ or -] 26b Alk. phosphatase ([micro]g [g.sup.-1] [h.sup.-1]) 8888 [+ or -] 163 lab 7154 [+ or -] 789ab n: HT 29 ST 16 Physical indicators Clay (%) 16 [+ or -] 1.2b 18 [+ or -] 2.0ab Silt (%) 33 [+ or -] 2.6 30 [+ or -] 3.5 Sand (%) 51 [+ or -] 3.7 53 [+ or -] 5.7 Gravel (%) 2.5 [+ or -] 0.7 6.7 [+ or -] 2.7 Bulk density (kg [m.sup.-3]) 1374 [+ or -] 37ab 1292 [+ or -] 52b Penetration resistance (kPa) 2101 [+ or -] 150ab 2132 [+ or -] 215ab WHC (mm [m.sup.-1]) 138 [+ or -] 3.0 133 [+ or -] 4.4 Chemical indicators pH 5.80 [+ or -] 0.11 6.00 [+ or -] 0.17 EA (emol [kg.sup.-1]) 0.67 [+ or -] 0.15 0.36 [+ or -] 0.12 Ca (mg [kg.sup.-1]) 1511 [+ or -] 115ab 1691 [+ or -] 192a Mg (mg [kg.sup.-1]) 249 [+ or -] 23c 429 [+ or -] 58b K (mg [kg.sup.-1]) 187 [+ or -] 25c 388 [+ or -] 78ab Na (mg [kg.sup.-1]) 86.6 [+ or -] 12.8bc 134 [+ or -] 29.4ab CEC (emol [kg.sup.-1]) 11.8 [+ or -] 0.9 11.3 [+ or -] 1.2 P (mg [kg.sup.-1]) 150 [+ or -] 20b 248 [+ or -] 48a Cu (mg [kg.sup.-1]) 1.83 [+ or -] 0.21 c 2.99 [+ or -] 0.50ab Zn (mg [kg.sup.-1]) 9.4 [+ or -] 1.5b 28.1 [+ or -] 6.7a Mn (mg [kg.sup.-1]) 30.9 [+ or -] 5.4 b 80.6 [+ or -] 21.1 a B (mg [kg.sup.-1]) 0.57 [+ or -] 0.04bc 0.76 [+ or -] 0.09a Biological indicators SOM (%) 4.4 [+ or -] 0.7b 5.0 [+ or -] 1.2ab SOC (%) 3.2 [+ or -] 0.3d 4.2 [+ or -] 0.5cd Active C (mg [kg.sup.-1]) 2357 [+ or -] 63b 2431 [+ or -] 96ab Total N (%) 0.34 [+ or -] 0.02c 0.43 [+ or -] 0.02b PMN ([micro]g N [g.sup.-1] [week.sup.-1]) 25.1 [+ or -] 1.8 31.3 [+ or -] 3.3 (3-glucosidase ([micro]g [g.sup.-1] [h.sup.-1]) 2961 [+ or -] 339bc 4375 [+ or -] 752a Urease ([micro]g [g.sup.-1] [h.sup.-1]) 113.8 [+ or -] 12.3b 208.6 [+ or -] 33.9a Acid phosphatase ([micro]g [g.sup.-1] [h.sup.-1]) 1461 [+ or -] 39ab 1336 [+ or -] 53bc Alk. phosphatase ([micro]g [g.sup.-1] [h.sup.-1]) 3744 [+ or -] 581 be 9088 [+ or -] 2114a n: DT 15 P-value Physical indicators Clay (%) 23 [+ or -] 2.7a 0.007 Silt (%) 28 [+ or -] 3.3 0.216 Sand (%) 46 [+ or -] 4.7 0.159 Gravel (%) 3.3 [+ or -] 1.3 0.134 Bulk density (kg [m.sup.-3]) 1504 [+ or -] 59a <0.001 Penetration resistance (kPa) 2294 [+ or -] 242a <0.001 WHC (mm [m.sup.-1]) 132 [+ or -] 4.3 0.482 Chemical indicators pH 5.42 [+ or -] 0.15 0.107 EA (emol [kg.sup.-1]) 0.48 [+ or -] 0.16 0.597 Ca (mg [kg.sup.-1]) 1146 [+ or -] 125b 0.013 Mg (mg [kg.sup.-1]) 206 [+ or -] 27c <0.001 K (mg [kg.sup.-1]) 119 [+ or -] 23d <0.001 Na (mg [kg.sup.-1]) 60.8 [+ or -] 12.9c 0.012 CEC (emol [kg.sup.-1]) 10.7 [+ or -] 1.2 0.113 P (mg [kg.sup.-1]) 99 [+ or -] 19b <0.001 Cu (mg [kg.sup.-1]) 1.88 [+ or -] 0.30bc <0.001 Zn (mg [kg.sup.-1]) 7.6 [+ or -] 1.8b <0.001 Mn (mg [kg.sup.-1]) 88.0 [+ or -] 22.2 a 0.004 B (mg [kg.sup.-1]) 0.46 [+ or -] 0.05c 0.002 Biological indicators SOM (%) 3.9 [+ or -] 0.9b 0.048 SOC (%) 2.3 [+ or -] 0.3e <0.001 Active C (mg [kg.sup.-1]) 2316 [+ or -] 89b 0.009 Total N (%) 0.30 [+ or -] 0.03c <0.001 PMN ([micro]g N [g.sup.-1] [week.sup.-1]) 25.7 [+ or -] 2.6 0.058 (3-glucosidase ([micro]g [g.sup.-1] [h.sup.-1]) 2140 [+ or -] 329c 0.004 Urease ([micro]g [g.sup.-1] [h.sup.-1]) 110.8 [+ or -] 16.1b <0.001 Acid phosphatase ([micro]g [g.sup.-1] [h.sup.-1]) 1515 [+ or -] 54a <0.001 Alk. phosphatase ([micro]g [g.sup.-1] [h.sup.-1]) 2114 [+ or -] 440c <0.001 Table 2. Means of the Shannon--Weaver and Evenness indices, which distinguish the soil microbial diversity of five tillage treatments of pastures in the southern Cape region of South Africa ND, No disturbance; KR, kikuyu-ryegrass; HT, herbicide-treated pasture; ST, shallow tillage; DT, deep tillage. Within columns, values followed by the same letter are not significantly different (P > 0.05) Shannon-Weaver index (H') Evenness (E) index ST 2.59a 0.82a DT 2.65ab 0.82a HT 2.67ab 0.84ab ND 2.76bc 0.85bc KR 2.80c 0.86c Table 3. Carbon-source utilisation profiles of total carbon-source groups for five tillage treatments ND, No disturbance; KR, kikuyu-ryegrass; HT, herbicide-treated pasture; ST, shallow tillage; DT, deep tillage. Within columns, values followed by the same letter are not significantly different (P > 0.05) Carbohydrates Carboxylic Amino Polymers Phenolic Amines acids acids compounds HT 10.07a 9.12a 6.46b 3.16a 1.05b 1.07ab ND 10.62a 9.48a 5.10a 3.49a 0.92ab 1.39a KR 10.24a 9.72a 5.35a 3.49a 0.88ab 1.32a DT 11.91a 9.45a 4.70a 3.25a 0.71a 0.98ab ST 12.23a 8.52a 5.68ab 2.92a 0.76ab 0.89b Table 4. Carbon-source utilisation profiles of individual carbon substrates under five tillage treatments ND, No disturbance; KR, kikuyu ryegrass; HT, herbicide-treated pasture; ST, shallow tillage; DT, deep tillage. Within rows, values followed by the same letter are not significantly different (P > 0.05) ND KR HT ST DT Carbohydrates [beta]-Methyl-D- 1.38a 1.15a 0.83a 1.12a 1.46a glucoside D-Galactonic acid 2.70ab 2.35ab 2.05ac 1.62c 2.95b (y-lactone) D-Xylose 0.11a 0.16a 0.17a 0.03a 0.10a i-Erythritol 0.11a 0.25a 0.13a 0.29a 0.26a D-Mannitol 1.43a 1.85ab 1.85ab 1.58a 2.40b N-Acetyl-D-glucosamine 1.82a 1.90a 1.83a 3.17b 2.44ab D-Celiobiose 1.60a 1.09a 1.32a 3.17b 0.87a Glucose-1 -phosphate 0.43a 0.69a 1.00a 0.27a 0.97a [alpha]-D-Lactose 0.43a 0.25a 0.10a 0.14a 0.11a D,L-[alpha]-Glycerol 0.60ab 0.56ab 0.79a 0.83a 0.36b phosphate Carboxylic acids Pyruvic acid methyl 1.67a 1.80a 1.95a 1.55a 1.92a ester D-Galacturonic acid 4.34a 4.02a 3.77a 4.30a 4.21a y-Hydroxybutyric acid 0.63a 0.71a 0.52a 0.53a 0.56a D-Glucosaminic acid 0.90a 0.96a 1.18a 0.91a 1.17a Itaconic acid 0.83a 0.93a 0.66a 0.75a 0.57a [alpha]-Ketobutyric acid 0.02a 0.04a 0.01a 0.15b 0.01a D-Malic acid 1.09a 1.27a 1.10a 0.33a 1.02a Amino acids L-Arginine 1.11ab 1.19ab 1.32b 1.22ab 0.87a L-Asparagine 2.34abc 2.50bc 2.89c 1.88a 2.20ab L-Phenylalanine 0.20a 0.17a 0.28a 0.79b 0.19a L-Serine 1.09a 1.11a 1.29a 1.08a 1.13a L-Threonine 0.02a 0.04a 0.21b 0.06a 0.03a Glycyl-L-glutamic acid 0.34a 0.34a 0.47a 0.65a 0.29a Polymers Tween 40 1.64a 1.73a 1.70 a 1.37a 1.50a Tween 80 1.56a 1.34a 1.32a 1.24a 1.39a [alpha]-Cyclodextrin 0.09ab 0.05ab 0.02a 0.12ab 0.21b Glycogen 0.20ab 0.37b 0.12a 0.19ab 0.15ab Phenolic compounds 2-Hydroxy benzoic acid 0.08b 0.01a 0.05ab 0.01ab 0.01ab 4-Hydroxy benzoic acid 0.84ab 0.87ab 1.00b 0.76ab 0.70a Amines Phenylethylamine 0.58c 0.48bc 0.25a 0.27abc 0.22ab Putrescine 0.81a 0.84a 0.82a 0.62a 0.76a Table 5. Correlation analysis between the soil quality assessment methods of the soil management assessment framework (SMAF) and its four sector soil quality indices and soil quality index for pastures (SQIP) in the southern Cape region of South Africa Sector scores for soil quality indices was calculated for soil physical quality (PQ) from bulk density and water-holding capacity; for chemical quality (CQ) from extractable P, exchangeable K, pH and the sodium adsorption ratio; and for biological quality (BQ) from potentially mineralisable N, organic C and [beta]-glucosidase. The sector score for SMAF fertility only took extractable P and exchangeable K into account. Values in bold denote a significant correlation (P < 0.05) SQIP SMAF SMAF SMAF SMAF SMAF index PQ CQ BQ fertility SQIP -- SMAF index 0.05 -- SMAF PQ 0.21# 0.63# -- SMAF CQ -0.08 0.34# -0.08 -- SMAF BQ 0.01 0.58# 0.46# -0.17 -- SMAF fertility -0.13 0.27# -0.11 0.95# -0.18 -- Note: Values denote a significant correlation (P < 0.05) indicate with #.
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|Author:||Swanepoel, P.A.; du Preez, C.C.; Botha, P.R.; Snyman, H.A.; Habig, J.|
|Date:||May 1, 2015|
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